Intelligent data curation

ABSTRACT

An apparatus includes a processor to: provide a set of feature routines to a set of processor cores to detect features of a data set distributed thereamong; generate metadata indicative of the detected features; generate context data indicative of contextual aspects of the data set; provide the metadata and context data to each processor core, and distribute a set of suggestion models thereamong to enable derivation of a suggested subset of data preparation operations to be suggested to be performed on the data set; transmit indications of the suggested subset to a viewing device, and receive therefrom indications of a selected subset of data preparation operations selected to be performed; compare the selected and suggested subsets; and in response to differences therebetween, re-train at least one suggestion model of the set of suggestion models based at least on the combination of the metadata, context data and selected subset.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of, and claims the benefit ofpriority under 35 U.S.C. § 120 to, U.S. patent application Ser. No.16/503,742 filed Jul. 5, 2019, and entitled “Intelligent Data Curation”;which claims the benefit of priority under 35 U.S.C. § 119(e) to U.S.Provisional Application Ser. No. 62/745,544 filed Oct. 15, 2018, andentitled “Data Characterization and Enrichment Recommendation Engine”;and to U.S. Provisional Application Ser. No. 62/840,083 filed Apr. 29,2019, and entitled “Intelligent Data Curation”; each of which isincorporated herein by reference in its entirety for all purposes.

BACKGROUND

In the handling of large data sets (what is frequently referred to as“big data”), the work of preparing data sets for analysis and/or forpresentation in reports and/or visualizations can consume more timeand/or more processing resources than the work of either of the analysesor the generation of presentations. As the size and number of data setscontinues to increase, the correspondingly increasing variety of usesfor data sets brings about a growing variety of data preparationoperations that may need to be performed and each data preparationoperation takes ever longer to perform. As a result, bottlenecks mayoccur in the preparation of data sets that may greatly delay theavailability of properly prepared data sets for subsequent analysisand/or presentation operations.

This has rendered such past practices as choosing to regularly perform aselected battery of data preparation operations on every data set,regardless of which data preparation operations are actually needed,increasingly unfeasible. The task of determining what data preparationoperations actually need to be performed on each data set has becomeincreasingly important.

Unfortunately, the increasing size of data sets also increases thedifficulty in relying on personnel to manually select the datapreparation operations that are to be performed on each data set.Manually inspecting even a large enough portion of data set to identifywhat data preparation operations are needed becomes increasinglydifficult and requires ever more time per data set. Additionally, theincreasing variety of data preparation operations that may need to beperformed to accommodate an increasing variety of uses for data sets canbecome overwhelming.

SUMMARY

This summary is not intended to identify only key or essential featuresof the described subject matter, nor is it intended to be used inisolation to determine the scope of the described subject matter. Thesubject matter should be understood by reference to appropriate portionsof the entire specification of this patent, any or all drawings, andeach claim.

An apparatus includes a processor and a storage to store instructionsthat, when executed by the processor, cause the processor to performoperations including; or a computer-program product tangibly embodied ina non-transitory machine-readable storage medium includes instructionsoperable on the processor to cause the processor to perform operationsincluding: receive an indication of availability of a data set, whereinthe indication of availability of the data set comprises an indicationof at least one of a contextual aspect of the data set from among apre-selected set of contextual aspects, a structural feature of the dataset from among a pre-selected set of structural features, or a datafeature of a data value of the data set from among a pre-selected set ofdata features; coordinate a performance of a distribution of data setportions of the data set among a set of processor cores; provide a setof feature routines to each processor core of the set of processor coresto enable each processor core of the set of processor cores to executeinstructions of each feature routine to detect a structural feature ofthe corresponding data set portion or a data feature of data values ofthe corresponding data set portion; receive indications of detectedstructural features and detected data features of the set of data setportions from the set of processor cores; generate, for the data set,metadata indicative of the detected structural features and the detecteddata features; generate, for the data set, context data indicative ofthe set of contextual aspects of the data set; provide the metadata andcontext data to each processor core of the set of processor cores;distribute a set of suggestion models among the set of processor coresto provide each processor core of the set of processor cores with adifferent suggestion model from among the set of suggestion models, andto enable the set of processor cores to employ the set of suggestionmodels to derive a suggested subset of data preparation operations of aset of data preparation operations to be suggested to be performed onthe data set, wherein each suggestion model comprises a pre-selectedtype of model previously trained to determine whether to suggest that acorresponding data preparation operation of the set of data preparationoperations be performed on the data set based on the metadata andcontext data; receive indications of the suggested subset from the setof processor cores; transmit an indication of the suggested subset to aviewing device to enable a presentation of the suggested subset;receive, from the viewing device, an indication of a selected subset ofthe set of data preparation operations selected to be performed; comparethe selected subset to the suggested subset to determine whether thereis a difference between the suggested and selected subsets; and inresponse to a determination that there is a difference between thesuggested and selected subsets, re-train at least one suggestion modelof the set of suggestion models based at least on a combination of themetadata, the context data and the selected subset.

The processor may be caused to provide an indication of the selectedsubset to each processor core of the selected set of processor cores toenable the set of processor cores to perform the selected subset of datapreparation operations on the data set.

The at least one suggestion model may include neural networkconfiguration data that comprises an indication of at least one of aquantity of neurons, a quantity of rows of neurons, a set of connectionsamong neurons, a trigger function to be implemented by at least oneneuron, weights and biases of the trigger function; and use of the atleast one model by at least one processor core of the set of processorcores to determine whether to suggest that the corresponding datapreparation operation be performed on the data set may include use ofthe suggestion model by the at least one processor core to configure aneural network to implement a set of classifiers. The re-training of theat least one suggestion model may include use, by the at least oneprocessor core, of at least the combination of the metadata, the contextdata and selected subset to perform backpropagation on the neuralnetwork to change at least one of the weights or the biases.

The set of processor cores may be distributed among a set of nodedevices, and the processor may be caused to perform operationsincluding: receive indications of availability of each node device of aplurality of node devices; and select the set of node devices from amongthe plurality of node devices based on the received indications ofavailability. The coordination of the performance of distribution of thedata set portions of the data set among the set of processor cores mayinclude a coordination of a performance of a distribution of the dataset portions among the set of node devices from at least one storagedevice through a network; the provision of the set of feature routinesto each processor core of the set of processor cores may includetransmission of the set of feature routines to each node device of theset of node devices; the provision of the metadata and context data toeach processor core of the set of processor cores may includetransmission of the metadata and context data to each node device of theset of node devices; and the distribution of the set of suggestionmodels among the set of processor cores may include the distribution ofthe set of suggestion models among the set of node devices to provideeach node device of the set of node devices with a different suggestionmodel from among the set of suggestion models.

Each node device of the set of node devices may store the correspondingdata set portion distributed to the node device after the detection ofstructural features or data features of the corresponding data portion;and the processor may be caused to transmit, to each node device of theset of node devices, an indication of the selected subset to enable thenode device to perform the selected subset of the set of datapreparation operations on the corresponding data set portion. Thepre-selected set of structural features of the data set may include asize of the data set; the indication of availability of the data set mayinclude an indication of the size of the data set; and the processor maybe caused to perform operations including derive a quantity of nodedevices based on the size of the data set, and select the quantity ofnode devices from among the plurality of node devices as the set of nodedevices. The processor may be caused to perform operations including:receive an indication from each node device of the plurality of nodedevices of a type of processor of the node device; and select the set offeature routines to transmit to each node device of the set of nodedevices based on the type of processor of the node device.

The processor may be caused to perform operations including: receiveupdate data comprising a feature routine; coordinate a distribution oftraining data set portions of a training data set among the set of nodedevices; transmit the set of feature routines that includes the receivedfeature routine to each node device of the set of node devices to enableeach node device of the set of node devices to detect features of thecorresponding training data set portion; receive indications of detectedfeatures of the set of training data set portions from the set of nodedevices; and generate, for the training data set, a portion of trainingmetadata indicative of the detected features. The processor may also becaused to transmit, to each node device of the set of node devices, theportion of the training metadata, a portion of training context data anda set of action indications, wherein: the portion of the trainingcontext data is indicative of a set of contextual aspects of thetraining data set; and the set of action indications is indicative of asubset of data preparation actions of a set of data preparation actionsto be suggested to be performed on the training data set based on theportion of the training metadata and the portion of the training contextdata. The processor may be further caused to perform operationsincluding: distribute the set of suggestion models among the set of nodedevices to provide each node device of the set of node devices with adifferent suggestion model from among the set of suggestion models, andto enable a re-training of the set of suggestion models by the set ofnode devices; receive, from each node device, a corresponding suggestionmodel of the set of suggestion models after the re-training; and storethe received suggestion models as replacements for the set of suggestionmodels.

The pre-selected set of contextual aspects may include at least one of:an indication of an identity of a source of the data set; an indicationof a location associated with the source; an indication of an industryassociated with the source; an indication of a time or date of receiptof the data set; an indication of a user of the other device; anindication of a location associated with the user; an indication of anindustry associated with the user; or an indication of a time or date ofreceipt of a request from the user to access the data set. Thepre-selected set of structural features may include at least one of: anindication of a size of the data set; an indication of a time or date ofgeneration of the data set; an indication of a language of the data set;an indication of an organization of data values within the data set; oran indication of a number of dimensions of indexing of the data set. Thepre-selected set of data features may include at least one of: anindication of at least one data type within the data set; an indicationof at least one data format within the data set; or an indication of aminimum, a maximum, a mean or an average of a data value of the dataset.

A computer-implemented method includes: receiving, by a processor, anindication of availability of a data set, wherein the indication ofavailability of the data set comprises an indication of at least one ofa contextual aspect of the data set from among a pre-selected set ofcontextual aspects, a structural feature of the data set from among apre-selected set of structural features, or a data feature of a datavalue of the data set from among a pre-selected set of data features;coordinating, by the processor, a performance of a distribution of dataset portions of the data set among a set of processor cores; providing,by the processor, a set of feature routines to each processor core ofthe set of processor cores to enable each processor core of the set ofprocessor cores to execute instructions of each feature routine todetect a structural feature of the corresponding data set portion or adata feature of data values of the corresponding data set portion;receiving, by the processor, indications of detected structural featuresand detected data features of the set of data set portions from the setof processor cores; generating, by the processor, for the data set,metadata indicative of the detected structural features and the detecteddata features; generating, by the processor, for the data set, contextdata indicative of the set of contextual aspects of the data set;providing, by the processor, the metadata and context data to eachprocessor core of the set of processor cores; distributing, by theprocessor, a set of suggestion models among the set of processor coresto provide each processor core of the set of processor cores with adifferent suggestion model from among the set of suggestion models, andto enable the set of processor cores to employ the set of suggestionmodels to derive a suggested subset of data preparation operations of aset of data preparation operations to be suggested to be performed onthe data set, wherein each suggestion model comprises a pre-selectedtype of model previously trained to determine whether to suggest that acorresponding data preparation operation of the set of data preparationoperations be performed on the data set based on the metadata andcontext data; receiving, by the processor, indications of the suggestedsubset from the set of processor cores; transmitting, by the processorand via a network, an indication of the suggested subset to a viewingdevice to enable a presentation of the suggested subset; receiving, bythe processor, and from the viewing device via the network, anindication of a selected subset of the set of data preparationoperations selected to be performed; comparing, by the processor, theselected subset to the suggested subset to determine whether there is adifference between the suggested and selected subsets; and in responseto a determination that there is a difference between the suggested andselected subsets, re-training, by the processor, at least one suggestionmodel of the set of suggestion models based at least on a combination ofthe metadata, the context data and the selected subset.

The method may further include providing, by the processor, anindication of the selected subset to each processor core of the selectedset of processor cores to enable the set of processor cores to performthe selected subset of data preparation operations on the data set.

The at least one suggestion model may include neural networkconfiguration data that comprises an indication of at least one of aquantity of neurons, a quantity of rows of neurons, a set of connectionsamong neurons, a trigger function to be implemented by at least oneneuron, weights and biases of the trigger function; and use of the atleast one model by at least one processor core of the set of processorcores to determine whether to suggest that the corresponding datapreparation operation be performed on the data set may include use ofthe suggestion model by the at least one processor core to configure aneural network to implement a set of classifiers. The re-training of theat least one suggestion model may include use, by the at least oneprocessor core, of at least the combination of the metadata, the contextdata and selected subset to perform backpropagation on the neuralnetwork to change at least one of the weights or the biases.

The set of processor cores may be distributed among a set of nodedevices, and the method may further include receiving, by the processor,indications of availability of each node device of a plurality of nodedevices; and selecting, by the processor, the set of node devices fromamong the plurality of node devices based on the received indications ofavailability. The coordination of the performance of distribution of thedata set portions of the data set among the set of processor cores mayinclude a coordination of a performance of a distribution of the dataset portions among the set of node devices from at least one storagedevice through a network; the provision of the set of feature routinesto each processor core of the set of processor cores may includetransmission of the set of feature routines to each node device of theset of node devices; the provision of the metadata and context data toeach processor core of the set of processor cores may includetransmission of the metadata and context data to each node device of theset of node devices; and the distribution of the set of suggestionmodels among the set of processor cores may include the distribution ofthe set of suggestion models among the set of node devices to provideeach node device of the set of node devices with a different suggestionmodel from among the set of suggestion models.

Each node device of the set of node devices may store the correspondingdata set portion distributed to the node device after the detection ofstructural features or data features of the corresponding data portion;and the method may further include transmitting, by the processor, toeach node device of the set of node devices, an indication of theselected subset to enable the node device to perform the selected subsetof the set of data preparation operations on the corresponding data setportion. The pre-selected set of structural features of the data set mayinclude a size of the data set, the indication of availability of thedata set may include an indication of the size of the data set; and themethod may further include: deriving, by the processor, a quantity ofnode devices based on the size of the data set; and selecting, by theprocessor, the quantity of node devices from among the plurality of nodedevices as the set of node devices. The method may further include:receiving, by the processor, an indication from each node device of theplurality of node devices of a type of processor of the node device; andselecting, by the processor, the set of feature routines to transmit toeach node device of the set of node devices based on the type ofprocessor of the node device.

The method may further include: receiving, by the processor, update datacomprising a feature routine; coordinating, by the processor and throughthe network, a distribution of training data set portions of a trainingdata set among the set of node devices; transmitting, by the processor,the set of feature routines that includes the received feature routineto each node device of the set of node devices to enable each nodedevice of the set of node devices to detect features of thecorresponding training data set portion; receiving, by the processor,indications of detected features of the set of training data setportions from the set of node devices; and generating, by the processor,for the training data set, a portion of training metadata indicative ofthe detected features. The method may also include transmitting, by theprocessor, to each node device of the set of node devices, the portionof the training metadata, a portion of training context data and a setof action indications, wherein: the portion of the training context datais indicative of a set of contextual aspects of the training data set;and the set of action indications is indicative of a subset of datapreparation actions of a set of data preparation actions to be suggestedto be performed on the training data set based on the portion of thetraining metadata and the portion of the training context data. Themethod may further include: distributing, by the processor, the set ofsuggestion models among the set of node devices to provide each nodedevice of the set of node devices with a different suggestion model fromamong the set of suggestion models, and to enable a re-training of theset of suggestion models by the set of node devices; receiving, by theprocessor, and from each node device, a corresponding suggestion modelof the set of suggestion models after the re-training; and storing thereceived suggestion models as replacements for the set of suggestionmodels.

The pre-selected set of contextual aspects may include at least one of:an indication of an identity of a source of the data set; an indicationof a location associated with the source; an indication of an industryassociated with the source; an indication of a time or date of receiptof the data set; an indication of a user of the other device; anindication of a location associated with the user; an indication of anindustry associated with the user; or an indication of a time or date ofreceipt of a request from the user to access the data set. Thepre-selected set of structural features may include at least one of: anindication of a size of the data set; an indication of a time or date ofgeneration of the data set; an indication of a language of the data set;an indication of an organization of data values within the data set; oran indication of a number of dimensions of indexing of the data set. Thepre-selected set of data features may include at least one of: anindication of at least one data type within the data set; an indicationof at least one data format within the data set; or an indication of aminimum, a maximum, a mean or an average of a data value of the dataset.

An apparatus includes a processor and a storage to store instructionsthat, when executed by the processor, cause the processor to performoperations including; or a computer-program product tangibly embodied ina non-transitory machine-readable storage medium includes instructionsoperable on the processor to cause the processor to perform operationsincluding: receive update data comprising a feature routine, wherein thefeature routine comprises executable instructions to detect a structuralfeature of a data set from among a pre-selected set of structuralfeatures or a data feature of a data value of the data set from among apre-selected set of data features; coordinate a performance of adistribution of training data set portions of a training data set amonga set of processor cores; provide a set of feature routines to eachprocessor core of the set of processor cores to enable each processorcore of the set of processor cores to execute instructions of eachfeature routine to detect a structural feature of the correspondingtraining data set portion or a data feature of data values of thecorresponding training data set portion; receive indications of detectedstructural features and detected data features of the set of trainingdata set portions from the set of processor cores; generate, for thetraining data set, training metadata indicative of the detectedstructural features and detected data features of the set of trainingdata set portions from the set of processor cores. The processor is alsocaused to provide the training metadata, a training context data and aset of action indications to each processor core of the set of processorcores, wherein: the training context data is indicative of apre-selected set of contextual aspects; and the set of actionindications is indicative of a subset of data preparation operations ofa set of data preparation operations to be suggested to be performed onthe training data set based on the training metadata and the trainingcontext data. The processor is further caused to distribute a set ofsuggestion models among the set of processor cores to provide eachprocessor core of the set of processor cores with a different suggestionmodel from among the set of suggestion models, and to enable the set ofprocessor cores to re-train the set of suggestion models, wherein: eachsuggestion model comprises a pre-selected type of model previouslytrained to determine whether to suggest that a corresponding datapreparation operation of the set of data preparation actions beperformed on a data set based on corresponding metadata and contextdata; the corresponding metadata is indicative of detected structuralfeatures and data features of the data set; and the correspondingcontext data is indicative of contextual aspects of the data set. Theprocessor is still further caused to receive, from each processor core,a corresponding suggestion model of the set of suggestion models afterthe re-training; and store the received suggestion models asreplacements for the set of suggestion models.

Space may be allocated within the training metadata to store a separateindication of whether each structural feature of the set of structuralfeatures or data feature of the set of data features that is detected inthe training data by a corresponding feature routine of the set offeature routines. The processor may be caused to determine whether thestructural feature or data feature detected by the received featureroutine comprises a structural feature that is already among the set ofstructural features or a data feature that is already among the set ofdata features. In response to a determination that the structuralfeature or data feature detected by the received feature routineincludes a new structural feature that is not already among the set ofstructural features or a new data feature that is not already among theset of data features, the processor may be caused to perform operationsincluding: store the received feature routine as an addition to the setof feature routines prior to the transmission of the set of featureroutine to each processor core of the set of processor cores to detectstructural features and data features of the training data set; andallocate another space in the training metadata to store a separateindication of whether the new structural feature or new data feature isdetected in the training data. The processor may be caused to, inresponse to a determination that the structural feature or data featuredetected by the received feature routine includes a structural featurethat is already among the set of structural features or a data featurethat is already among the set of data features, replace a correspondingfeature routine among the set of feature routines that detects thestructural feature or data feature with the received feature routineprior to the transmission of the set of feature routines to the set ofnode devices to detect features of the training data set.

For each data preparation operation of the set of data preparationoperations, space may be allocated within the set of action indicationsfor a corresponding subset of action indications; for each subset ofaction indications, each action indication may correspond to a differentcombination of training data set and context data; and the update datacomprises a suggestion model. The processor may be caused to determinewhether the data preparation operation that corresponds to the receivedsuggestion model comprises a data preparation operation that is alreadyamong the set of data preparation operations. In response to adetermination that the corresponding data preparation operation includesa new data preparation operation that is not already among the set ofdata preparation operations, the processor may be caused to performoperations including: store the suggestion model as an addition to theset of suggestion models prior to the distribution of the set ofsuggestion models among the set of processor cores to enable re-trainingof the set of suggestion models; and allocate another space within theset of action indications for another subset of action indications thatcorresponds to the data preparation operation that corresponds to thereceived suggestion model. The processor may be caused to, in responseto a determination that the corresponding data preparation operationincludes a data preparation operation that is already among the set ofdata preparation operations, replace a corresponding suggestion modelamong the set of suggestion models that determines whether to suggestthe data preparation operation prior to the distribution of the set ofsuggestion models among the set of processor cores to enable re-trainingof the set of suggestion models.

At least one suggestion model of the set of suggestion models mayinclude neural network configuration data that comprises an indicationof at least one of a quantity of neurons, a quantity of rows of neurons,a set of connections among neurons, a trigger function to be implementedby at least one neuron, weights and biases of the trigger function; anduse of the at least one model by at least one processor core of the setof processor cores to determine whether to suggest that thecorresponding data preparation operation be performed on the data setmay include use of the suggestion model by the at least one processorcore to configure a neural network to implement a set of classifiers.The re-training of the at least one suggestion model may include use, bythe at least one processor core, of at least the combination of thetraining metadata, the training context data and a subset of actionindications of the set of action indications that corresponds to thecorresponding data preparation action to perform backpropagation on theneural network to change at least one of the weights or the biases.

The set of processor cores may be distributed among a set of nodedevices, and the processor may be caused to perform operationsincluding: receive indications of availability of each node device of aplurality of node devices; and select the set of node devices from amongthe plurality of node devices based on the received indications ofavailability. The coordination of the performance of distribution of thetraining data set portions of the data set among the set of processorcores may include a coordination of a performance of a distribution ofthe training data set portions among the set of node devices from atleast one storage device through a network; the provision of the set offeature routines to each processor core of the set of processor coresmay include transmission of the set of feature routines to each nodedevice of the set of node devices; the provision of the trainingmetadata, the training context data and the set of action indications toeach processor core of the set of processor cores may includetransmission of the training metadata, the training context data and theset of action indications to each node device of the set of nodedevices; and the distribution of the set of suggestion models among theset of processor cores may include the distribution of the set ofsuggestion models among the set of node devices to provide each nodedevice of the set of node devices with a different suggestion model fromamong the set of suggestion models.

The processor may be caused to perform operations including: receive anindication of availability of the data set, wherein the indication ofavailability of the data set comprises an indication of at least one ofa contextual aspect of the data set from among the set of contextualaspects, a structural feature of the data set from among the set ofstructural features, or a data feature of a data value of the data setfrom among the set of data features; coordinate a performance of adistribution of data set portions of the data set among the set of nodedevices; transmit the set of feature routines to each node device of theset of node devices to enable each node device of the set of nodedevices to detect a structural feature of the corresponding data setportion or a data feature of data values of the corresponding data setportion; receive indications of detected structural features anddetected data features of the set of data set portions from the set ofnode devices; generate, for the data set, metadata indicative of thedetected structural features and the detected data features; generate,for the data set, context data indicative of the set of contextualaspects of the data set; transmit the metadata and context data to eachnode device of the set of node devices; distribute the set of suggestionmodels among the set of node devices to provide each node device of theset of node devices with a different suggestion model from among the setof suggestion models, and to enable the set of node devices to employthe set of suggestion models to derive a suggested subset of datapreparation operations of a set of data preparation operations to besuggested to be performed on the data set; receive indications of thesuggested subset from the set of node devices; transmit an indication ofthe suggested subset to a viewing device to enable a presentation of thesuggested subset; receive, from the viewing device, an indication of aselected subset of the set of data preparation operations selected to beperformed; compare the selected subset to the suggested subset todetermine whether there is a difference between the suggested andselected subsets; and in response to a determination that there is adifference between the suggested and selected subsets, and inpreparation for the re-training of the set of suggestion models, performoperations including store the metadata as an addition to the trainingmetadata, store the context data as an addition to the training contextdata, and store an indication of the selected subset as an addition tothe set of action indications.

The pre-selected set of contextual aspects may include at least one of:an indication of an identity of a source of the data set; an indicationof a location associated with the source; an indication of an industryassociated with the source; an indication of a time or date of receiptof the data set; an indication of a user of the other device; anindication of a location associated with the user; an indication of anindustry associated with the user; or an indication of a time or date ofreceipt of a request from the user to access the data set. Thepre-selected set of structural features may include at least one of: anindication of a size of the data set; an indication of a time or date ofgeneration of the data set; an indication of a language of the data set;an indication of an organization of data values within the data set; oran indication of a number of dimensions of indexing of the data set. Thepre-selected set of data features may include at least one of: anindication of at least one data type within the data set; an indicationof at least one data format within the data set; or an indication of aminimum, a maximum, a mean or an average of a data value of the dataset.

A computer-implemented method includes: receiving, by a processor,update data comprising a feature routine, wherein the feature routinecomprises executable instructions to detect a structural feature of adata set from among a pre-selected set of structural features or a datafeature of a data value of the data set from among a pre-selected set ofdata features; coordinating, by the processor, a performance of adistribution of training data set portions of a training data set amonga set of processor cores; providing, by the processor, a set of featureroutines to each processor core of the set of processor cores to enableeach processor core of the set of processor cores to executeinstructions of each feature routine to detect a structural feature ofthe corresponding training data set portion or a data feature of datavalues of the corresponding training data set portion; receiving, by theprocessor, indications of detected structural features and detected datafeatures of the set of training data set portions from the set ofprocessor cores; and generating, by the processor, for the training dataset, training metadata indicative of the detected structural featuresand detected data features of the set of training data set portions fromthe set of processor cores. The method also includes providing, by theprocessor, the training metadata, a training context data and a set ofaction indications to each processor core of the set of processor cores,wherein: the training context data is indicative of a pre-selected setof contextual aspects; and the set of action indications is indicativeof a subset of data preparation operations of a set of data preparationoperations to be suggested to be performed on the training data setbased on the training metadata and the training context data. The methodfurther includes distributing, by the processor, a set of suggestionmodels among the set of processor cores to provide each processor coreof the set of processor cores with a different suggestion model fromamong the set of suggestion models, and to enable the set of processorcores to re-train the set of suggestion models, wherein: each suggestionmodel comprises a pre-selected type of model previously trained todetermine whether to suggest that a corresponding data preparationoperation of the set of data preparation actions be performed on a dataset based on corresponding metadata and context data; the correspondingmetadata is indicative of detected structural features and data featuresof the data set; and the corresponding context data is indicative ofcontextual aspects of the data set. The method still further includesreceiving, by the processor and from each processor core, acorresponding suggestion model of the set of suggestion models after there-training; and storing the received suggestion models as replacementsfor the set of suggestion models.

Space may be allocated within the training metadata to store a separateindication of whether each structural feature of the set of structuralfeatures or data feature of the set of data features that is detected inthe training data by a corresponding feature routine of the set offeature routines. The method may further include determining, by theprocessor, whether the structural feature or data feature detected bythe received feature routine includes a structural feature that isalready among the set of structural features or a data feature that isalready among the set of data features. The method may further include,in response to a determination that the structural feature or datafeature detected by the received feature routine includes a newstructural feature that is not already among the set of structuralfeatures or a new data feature that is not already among the set of datafeatures, performing operations including: storing the received featureroutine as an addition to the set of feature routines prior to thetransmission of the set of feature routine to each processor core of theset of processor cores to detect structural features and data featuresof the training data set; and allocating another space in the trainingmetadata to store a separate indication of whether the new structuralfeature or new data feature is detected in the training data. The methodmay further include, in response to a determination that the structuralfeature or data feature detected by the received feature routineincludes a structural feature that is already among the set ofstructural features or a data feature that is already among the set ofdata features, replacing a corresponding feature routine among the setof feature routines that detects the structural feature or data featurewith the received feature routine prior to the transmission of the setof feature routines to the set of node devices to detect features of thetraining data set.

For each data preparation operation of the set of data preparationoperations, space may be allocated within the set of action indicationsfor a corresponding subset of action indications; for each subset ofaction indications, each action indication may correspond to a differentcombination of training data set and context data; and the update datamay include a suggestion model. The method may further includedetermining, by the processor, whether the data preparation operationthat corresponds to the received suggestion model comprises a datapreparation operation that is already among the set of data preparationoperations. The method may still further include, in response to adetermination that the corresponding data preparation operation includesa new data preparation operation that is not already among the set ofdata preparation operations, performing operations including: storingthe suggestion model as an addition to the set of suggestion modelsprior to the distribution of the set of suggestion models among the setof processor cores to enable re-training of the set of suggestionmodels; and allocating another space within the set of actionindications for another subset of action indications that corresponds tothe data preparation operation that corresponds to the receivedsuggestion model. The method may further include, in response to adetermination that the corresponding data preparation operationcomprises a data preparation operation that is already among the set ofdata preparation operations, replacing a corresponding suggestion modelamong the set of suggestion models that determines whether to suggestthe data preparation operation prior to the distribution of the set ofsuggestion models among the set of processor cores to enable re-trainingof the set of suggestion models.

At least one suggestion model of the set of suggestion models mayinclude neural network configuration data that includes an indication ofat least one of a quantity of neurons, a quantity of rows of neurons, aset of connections among neurons, a trigger function to be implementedby at least one neuron, weights and biases of the trigger function; anduse of the at least one model by at least one processor core of the setof processor cores to determine whether to suggest that thecorresponding data preparation operation be performed on the data setmay include use of the suggestion model by the at least one processorcore to configure a neural network to implement a set of classifiers.The re-training of the at least one suggestion model may include use, bythe at least one processor core, of at least the combination of thetraining metadata, the training context data and a subset of actionindications of the set of action indications that corresponds to thecorresponding data preparation action to perform backpropagation on theneural network to change at least one of the weights or the biases.

The set of processor cores may be distributed among a set of nodedevices, and the method may further include: receiving, by theprocessor, indications of availability of each node device of aplurality of node devices; and selecting, by the processor, the set ofnode devices from among the plurality of node devices based on thereceived indications of availability. The coordination of theperformance of distribution of the training data set portions of thedata set among the set of processor cores may include a coordination ofa performance of a distribution of the training data set portions amongthe set of node devices from at least one storage device through anetwork; the provision of the set of feature routines to each processorcore of the set of processor cores may include transmission of the setof feature routines to each node device of the set of node devices; theprovision of the training metadata, the training context data and theset of action indications to each processor core of the set of processorcores may include transmission of the training metadata, the trainingcontext data and the set of action indications to each node device ofthe set of node devices; and the distribution of the set of suggestionmodels among the set of processor cores may include the distribution ofthe set of suggestion models among the set of node devices to provideeach node device of the set of node devices with a different suggestionmodel from among the set of suggestion models.

The method may further include: receiving, by the processor, anindication of availability of the data set, wherein the indication ofavailability of the data set comprises an indication of at least one ofa contextual aspect of the data set from among the set of contextualaspects, a structural feature of the data set from among the set ofstructural features, or a data feature of a data value of the data setfrom among the set of data features; coordinating, by the processor andthrough a network, a performance of a distribution of data set portionsof the data set among the set of node devices; transmitting, by theprocessor, the set of feature routines to each node device of the set ofnode devices to enable each node device of the set of node devices todetect a structural feature of the corresponding data set portion or adata feature of data values of the corresponding data set portion;receiving, by the processor, indications of detected structural featuresand detected data features of the set of data set portions from the setof node devices; generating, by the processor, for the data set,metadata indicative of the detected structural features and the detecteddata features; generating, by the processor, for the data set, contextdata indicative of the set of contextual aspects of the data set;transmitting, by the processor, the metadata and context data to eachnode device of the set of node devices; distributing, by the processor,the set of suggestion models among the set of node devices to provideeach node device of the set of node devices with a different suggestionmodel from among the set of suggestion models, and to enable the set ofnode devices to employ the set of suggestion models to derive asuggested subset of data preparation operations of a set of datapreparation operations to be suggested to be performed on the data set;receiving, by the processor, indications of the suggested subset fromthe set of node devices; transmitting, by the processor and by thenetwork, an indication of the suggested subset to a viewing device toenable a presentation of the suggested subset; receiving, by theprocessor, and via the network from the viewing device, an indication ofa selected subset of the set of data preparation operations selected tobe performed; comparing, by the processor, the selected subset to thesuggested subset to determine whether there is a difference between thesuggested and selected subsets; and in response to a determination thatthere is a difference between the suggested and selected subsets, and inpreparation for the re-training of the set of suggestion models,performing operations including storing the metadata as an addition tothe training metadata, storing the context data as an addition to thetraining context data, and storing an indication of the selected subsetas an addition to the set of action indications.

The pre-selected set of contextual aspects may include at least one of:an indication of an identity of a source of the data set; an indicationof a location associated with the source; an indication of an industryassociated with the source; an indication of a time or date of receiptof the data set; an indication of a user of the other device; anindication of a location associated with the user; an indication of anindustry associated with the user; or an indication of a time or date ofreceipt of a request from the user to access the data set. Thepre-selected set of structural features may include at least one of: anindication of a size of the data set; an indication of a time or date ofgeneration of the data set; an indication of a language of the data set;an indication of an organization of data values within the data set; oran indication of a number of dimensions of indexing of the data set. Thepre-selected set of data features may include at least one of: anindication of at least one data type within the data set; an indicationof at least one data format within the data set; or an indication of aminimum, a maximum, a mean or an average of a data value of the dataset.

The foregoing, together with other features and embodiments, will becomemore apparent upon referring to the following specification, claims, andaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is described in conjunction with the appendedfigures:

FIG. 1 illustrates a block diagram that provides an illustration of thehardware components of a computing system, according to some embodimentsof the present technology.

FIG. 2 illustrates an example network including an example set ofdevices communicating with each other over an exchange system and via anetwork, according to some embodiments of the present technology.

FIG. 3 illustrates a representation of a conceptual model of acommunications protocol system, according to some embodiments of thepresent technology.

FIG. 4 illustrates a communications grid computing system including avariety of control and worker nodes, according to some embodiments ofthe present technology.

FIG. 5 illustrates a flow chart showing an example process for adjustinga communications grid or a work project in a communications grid after afailure of a node, according to some embodiments of the presenttechnology.

FIG. 6 illustrates a portion of a communications grid computing systemincluding a control node and a worker node, according to someembodiments of the present technology.

FIG. 7 illustrates a flow chart showing an example process for executinga data analysis or processing project, according to some embodiments ofthe present technology.

FIG. 8 illustrates a block diagram including components of an EventStream Processing Engine (ESPE), according to embodiments of the presenttechnology.

FIG. 9 illustrates a flow chart showing an example process includingoperations performed by an event stream processing engine, according tosome embodiments of the present technology.

FIG. 10 illustrates an ESP system interfacing between a publishingdevice and multiple event subscribing devices, according to embodimentsof the present technology.

FIG. 11 illustrates a flow chart showing an example process ofgenerating and using a machine-learning model according to some aspects.

FIG. 12 illustrates an example machine-learning model based on a neuralnetwork.

FIGS. 13A, 13B and 13C, together, illustrate an example embodiment of adistributed processing system.

FIG. 14 illustrates another example embodiment of a distributedprocessing system.

FIG. 15 illustrates an example embodiment of generation, exchanges anduse of pieces of data among devices of a distributed processing system.

FIGS. 16A, 16B, 16C and 16D, together, illustrate a more detailedexample embodiment of preparation of a distributed processing system fornormal use.

FIGS. 17A, 17B, 17C, 17D, 17E and 17F, together, illustrate a moredetailed example embodiment of feature detection and suggestion of datapreparation operations.

FIGS. 18A and 18B, together, illustrate a more detailed exampleembodiment of a re-training of a distributed processing system to extendand/or improve functionality.

FIGS. 19A, 19B, 19C and 19D, together, illustrate an example embodimentof a logic flow of a coordinating device preparing or updating adistributed processing system.

FIGS. 20A, 20B and 20C, together, illustrate an example embodiment of alogic flow of a coordinating device coordinating normal operation of adistributed processing system.

DETAILED DESCRIPTION

Various embodiments described herein are generally directed to adistributed processing system for selecting a subset of available datapreparation operations to suggest be performed on a data set based onthe detection of features of the data set, where the variety of featuresto be detected and/or the variety of data preparation operations to beavailable to be suggested is extensible. More precisely, in adistributed processing system, a data set may be divided into multipleportions that are distributed among a set of node devices to enablefeatures of each of those portions to be detected by the set of nodedevices at least partially in parallel. A coordinating device of thedistributed processing system may generate a portion of metadataindicative of the features detected in the data set based on indicationsreceived from the multiple node devices of features detected within eachof the data set portions. The coordinating device may also generate aportion of context data indicative of various aspects of the context ofthe data set, and may transmit the portions of the metadata and of thecontext data to each node device of the multiple node devices. A set ofsuggestion models that each correspond to a different data preparationoperation of a set of data preparation operations that are able to beperformed on a data set may be distributed among the set of nodedevices. Acting at least partially in parallel, the node devices of theset of node devices may use the portions of the metadata and the contextdata as inputs to the set of suggestion models to derive a subset of theset of data preparation operations to suggest be performed on the dataset. The coordinating device may provide an indication of the suggestedsubset of data preparation operations to another device to enable apresentation of the suggested subset thereby, and may await receipttherefrom of an indication of what subset of data preparation operationsof the set of data preparation operations is selected to be performed onthe data set. Upon receipt of the selected subset of data preparationoperations, the coordinating device may coordinate the performance ofthe selected subset of data preparation operations on the data set bythe node devices, at least partially in parallel. The coordinatingdevice may also compare the suggested subset of data preparationoperations to the selected subset. If there are differencestherebetween, then the coordinating device may add the combination ofthe portion of the metadata, the portion of the context data and theselected subset to a training data set by which the set of suggestionmodels may be retrained at a recurring interval of time or number ofinstances of generating a suggested subset.

The data of each data set may be any of a variety of types of data(e.g., societal statistics data, business operations data, raw data fromsensors of large scale experiments, financial data, medical treatmentanalysis data, data from geological or meteorogical instruments, streamsof data collected from Internet-attached appliances, etc.). By way ofexample, the data set may include scientific observation data concerninggeological and/or meteorological events, or from sensors laboratoryexperiments in areas such as particle physics. By way of anotherexample, the data set may include indications of activities performed bya random sample of individuals of a population of people in a selectedcountry or municipality, or of a population of a threatened speciesunder study in the wild. Each data set may be stored in a distributedmanner among a grid of storage devices of the distributed processingsystem. In some embodiments, the grid of storage devices may be employedto store numerous data sets as each data set is assembled from dataitems detected and/or collected by various source devices, and/or dataitems generated as an output of various analyses performed by varioussource devices.

Regardless of the exact manner in which each data set is formed, as adata set is formed to the point of completion and/or becomes availablein some other way, the coordinating device of the distributed processingsystem may receive an indication of its availability. In someembodiments, such an indication may be received from a storage devicethat stores at least a portion of the data set. It may be that thisarises from the coordinating device recurringly polling storage devicesto identify occurrences of the storage of new data sets therein.Alternatively or additionally, it may be that this arises from thecoordinating device receiving a request from a viewing device operatedby a user thereof to access the data set from where it is currentlystored within one or more storage devices such that the coordinatingdevice is made aware of the availability of the data set.

Depending on the exact manner in which the coordinating device is madeaware of the availability of the data set, in some embodiments, thecoordinating device may receive an initial amount of informationconcerning features and/or aspects of its context along with theindication of its availability, in some embodiments. Alternatively oradditionally, the coordinating device may query the one or more storagedevices in which the data set is stored to, itself, retrieve such aninitial amount of information. Among such features in such an initialamount of information may be an indication of the size of the data set,and/or among such aspects of its context may be an indication of itssource, when it was generated and/or within what storage device(s) it isstored. The coordinating device may use such information concerningaspects of the context of the data set to assign a higher or lowerpriority to the data set versus other data sets. By way of example, thesource of the data set and/or which storage device(s) in which it isstored may cause the data set to be assigned a high enough priority asto become the next selected data set despite a lengthy queue of otherdata sets being available. Alternatively or additionally, thecoordinating device may use such information concerning the feature ofsize of the data set, along with recurringly received indications ofwhich node devices of the distributed processing system are available todetermine how many node devices of the distributed processing system, aswell as which ones, to include in the set of node devices.

Regardless of the exact manner in which the data set becomes the nextselected data set, and regardless of the exact manner in which the setof node devices is selected, the coordinating device may communicatewith each of the node devices of the set of node devices as part ofeffecting the distribution of portions of the data set among the set ofnode devices. In some embodiments, the coordinating device may dividethe data set into data set portions of equal (or nearly equal) size aspart of distributing the processing and storage requirements of the dataset among the set of node devices relatively equally. The coordinatingdevice may transmit, to each node device of the set of node devices, apointer or other indication as to the storage location(s) within one ormore storage devices at which it may independently retrieve the data setportion assigned to it by the coordinating device. In other embodiments,the coordinating device may, itself, retrieve each data set portion fromone or more storage devices and relay each to the node device of the setof node devices to which the coordinating device has assigned it.

With the data set portions of the data set distributed among the set ofnode devices, the coordinating device may transmit a set of featureroutines to each node device of the set of node devices. Each featureroutine corresponds to a particular feature that the data set may have,and may include a set of instructions executable within a node device toanalyze a corresponding one of the data set portions to detect thecorresponding feature. Each of the node devices may execute each of thefeature routines of the set of feature routines to determine whether anyof the features detectable through such execution are present within thedata set portion assigned to that node device. The coordinating devicemay coordinate such execution of the set of feature routines by eachnode device of the set of node devices to occur at least partially inparallel. As such execution of the feature routines by the set of nodedevices occurs, each of the node devices of the set of node devices mayprovide indications of detected features to the coordinating device.

The features sought to be detected through the execution of the set offeature routines may include any of wide variety of features, includingand not limited to, structural features of the data set, features of theindexing scheme by which data values of the data set are able to belocated, and/or features of the data values, themselves. Thus, by way ofexample, the features to be so detected may include, and are not limitedto, punctuation types, delimiter types, region-specific formats,industry-specific formats, use of data containerization and/or accesscontrol, use of data compression and/or encryption, data types of thedata values, languages included, scripting and/or programming languagesincluded, arithmetic and/or logical operators, indexing type, indexlabels, current index ranges, data set size, date/time and/or indicationof author and/or owner. Where data values include numeric values, thefeatures to be so detected may also include various statistical values,including and not limited to, maximums, minimums, mean and/or median.

In some embodiments, the coordinating device may cooperate with the setof node devices to exchange at least a subset of the indications ofdetected features among the node devices within the set of node devices,and may do so in a manner similar to what is disclosed in U.S. Pat. No.9,753,767 issued Sep. 5, 2017, the disclosure of which is incorporatedherein by reference in its entirety. As discussed therein, the detectionof one or more features of the data set may be assisted by, guided byand/or triggered by whether one or more other features of the data sethave been detected. By way of example, the detection of one portion ofan indexing scheme used to organize data values within one data setportion may not be possible without another portion of the indexingscheme having been detected in another data set portion such that one ormore aspects of the indexing scheme (e.g., the type of indexing scheme,the location of components of the indexing scheme within the data set,etc.) are made known.

Regardless of whether such indications of detected features areexchanged, the coordinating device may generate a portion of metadatathat is indicative of features of the data set based on the indicationsof detected features received from the set of node devices. In someembodiments, such a portion of metadata may take the form of a featurevector of values indicative of the detected features. In someembodiments, those values may be limited to indications of simplywhether particular features have been found to be present. In otherembodiments, those values may include numerical indications ofquantities, measures, degrees, etc. of aspects of features found to bepresent, and/or may include indications of particular type, format,industry standard, revision level, etc. of aspects of features found tobe present.

With the portion of metadata (e.g., a feature vector) corresponding tothe data set having been generated, the coordinating device may generatea portion of context data that also corresponds to the data set based onindications of contextual aspects of the data set received from any of avariety of sources. In some embodiments, such indications may beprovided to the coordinating device as part of providing an indicationto the coordinating device of the availability of the data set. By wayof example, and as previously discussed, the coordinating device mayreceive a request to retrieve and/or otherwise provide access to thedata set from a viewing device, and the request may include variousindications of contextual aspects of the data set as part of providingthe coordinating device with the information needed to search for and/orgain access to the data set. In other embodiments, upon becoming awareof the availability of the data set, the coordinating device maytransmit a request to provide indications of contextual aspects of thedata set to the one or more storage devices in which the data set may bestored.

The contextual aspects of the data set that are included in the portionof context data may include any of a variety of aspects, including andnot limited to, aspects of when and how the data set was generated,aspects of the source of the data set and/or the data therein, aspectsof legal and/or other rights associated with the data set and/or thedata therein, etc. Thus, by way of example, the contextual aspects mayinclude, and are not limited to, the when, where, how, why and/or by whothe data set and/or the data therein was generated; where the data setis and/or has been stored; history of revisions to the data set; owners,creators, licensees, licensors, custodians, etc. of the data set; and/orcopyrights, licensing terms, publication conditions, accessrestrictions, etc. of the data.

In a manner analogous to the metadata generated for the data set, insome embodiments, the portion of context generated for the data set maytake the form of a context vector of values indicative of the contextualaspects of the data set. In some embodiments, those values may belimited to indications of simply whether each contextual aspect isapplicable to the data set. In other embodiments, those values mayinclude numerical indications of quantities, measures, degrees, etc. ofcontextual aspects that have been determined to be applicable. In stillother embodiments, those values may include portions of text and/orother encoded forms of character data that are descriptive of contextualaspects.

With the portion of metadata and the portion of context data (e.g., acontext vector) corresponding to the data set having been generated, thecoordinating device may transmit both to each node device of the set ofnode devices. The coordinating device may also distribute a set ofsuggestion models among the set of node devices, with each node devicereceiving one or more different suggestion models from the other nodedevices. Each suggestion model corresponds to a different particulardata preparation operation that may be performed on the data set fromamong a set of data preparation operations. Each suggestion model may beany of a variety of type of machine learning model (including any of avariety of types of decision tree), and each may have been previouslytrained to determine whether to suggest that its corresponding datapreparation operation be performed on a data set based on detectedfeatures and contextual aspects thereof. In some embodiments, at leastone of the suggestion models may be a contextual bandit decision treeselected to achieve a pre-selected balance between exploitation of pastsuccesses in determining whether the performance of the correspondingdata preparation operation is to be suggested, and exploration ofoccasions on which to test making an opposite determination from the onethat would be made based on exploitation in support of further machinelearning.

With the portions of metadata and context data transmitted to each ofthe node devices of the set of node devices, and with the set ofsuggestion models distributed among the node devices, each of the nodedevices may employ the portions of metadata and context data as inputsto each of the one or more suggestion models distributed to it to derivea separate determination from each suggestion model of whether itscorresponding data preparation operation is to be suggested to beperformed on the data set. The coordinating device may coordinate suchuses of the set of suggestion models by the set of node devices to occurat least partially in parallel. As such determinations are made, each ofthe node devices of the set of node devices may provide indications ofsuch determinations to the coordinating device.

The data preparation operations may include any of a variety of types ofoperations, including and not limited to: data value and/or formatnormalizations; data transformations; data filtering, stripping and/ormasking; and/or data various data analyses in support of the generationof various graphical presentations. Such operations may serve to changedata values, the selection of data values, the format of data values,the arrangement of data values within a data set, the structure of adata set, the indexing scheme of a data set, etc. Alternatively oradditionally, such operations may serve to remove data values forreasons of data security and/or to comply with data privacy (e.g.,legally mandated personal medical data privacy restrictions),intellectual property protections (e.g., copyright), licensing terms,etc. Any of such operations may be performed to cause a data set and/orthe data values thereof to fit what is needed for different geographicregions, different legal jurisdictions, different languages, differentindustries, different scientific fields, different entities (e.g.,convert among corporate, academic and/or governmental entities), etc.

From the indications received by the coordinating device of which datapreparation operations are to be suggested to be performed on the dataset, and which are not, the coordinating device may transmit anindication of a suggested subset of the set of data preparationoperations that are to be performed on the data set to another device toenable the presentation of the suggested subset to a user. The otherdevice may, itself, provide the user with a user interface by which itdirectly presents the suggested subset and awaits input from the user.In some embodiments, the other device may be a viewing device thatenables the user thereof to manually view (or otherwise inspect)portions and/or various aspects of the data set to determine whether theuser agrees with the suggested subset of data preparation operations.The user may provide input indicating that the suggested subset isselected to be the subset of data preparation operations that are to beperformed on the data set, or a different subset of the set of datapreparation operations is selected to be so performed.

Upon receipt, from the other device, of an indication of the selectedsubset of data preparation operations that are to be performed on thedata set, the coordinating device may coordinate the performance of theselected subset with the set of node devices. In some embodiments, eachnode device of the set of node devices may continue to store the dataset portion of the data set that was distributed to it as part of theaforedescribed detection of features. In such embodiments, and dependingon such factors as the amount of time that has elapsed since theaforedescribed detection of features, advantage may be taken of suchdistribution of the data set among the set of node devices by causingthe set of node devices to then perform the selected subset of datapreparation operations on those data set portions in situ.

Also upon receipt, from the other device, of the indication of theselected subset of data preparation operations to be performed on thedata set, the coordinating device may compare the selected subset to thesuggested subset to determine whether there are any differencestherebetween. If there are no differences, then the suggested subset maybe deemed to represent a set of successful determinations by thedistributed processing system of which data preparation operations areto be suggested to the user. In some embodiments, the coordinatingdevice 2500 may maintain a count, a score or other indication for eachsuggestion model that reflects the rate of the ability of eachsuggestion model to successfully make such determinations. Such anindication of success rate may be updated to reflect each instance of asuccess and/or lack thereof in making such a determination for eachsuggestion model, and such an indication may be employed as an input toany subsequent re-training of that suggestion model and/or of the set ofsuggestion models.

However, if there are differences between the suggested subset and theselected subset, then the coordinating device may add the portion ofmetadata, the portion of context data and an indication of the selectedsubset to a training data structure used in training the set ofsuggestion models. In some embodiments, whether the coordinating devicedoes add these items to the training data structure may be at leastpartially dependent on the choice of the overall machine learningalgorithm employed to improve the making of determinations of what datapreparation operations to suggest. As will be familiar to those skilledin the art, while the use of decision trees and/or similar models as thesuggestion models may be deemed relatively effective in making suchdeterminations, decision trees are subject to all too easily learningwrong lessons from occasional bad input. More precisely, there may beinstances in which the user provides a selected subset of the datapreparation operations that includes one or more errant selections of adata preparation operation to be performed or to not be performed. Theuse of decision trees may result in the suggestion models correspondingto those errantly selected or errantly non-selected data preparationoperations all too easily learning such mistakes, thereby resulting infuture incorrect determinations of whether to suggest the performance ofthose data preparation operations. To counter this, in some embodiments,any of a variety of sampling algorithms may be used to control whetherthe training data structure is to be augmented with the introduction ofnoise into the training set to increase the variance and reduce thepossibility of overfitting of the selected subset in response to theselected subset differing from the suggested subset. Such use ofsampling may be based on a presumption that, even though there may beoccasional mistakes made by a user in specifying a subset of the datapreparation operations to be performed on a data set, the user is morelikely to specify a correct subset on the majority of occasions. Thus,in essence, such use of sampling serves to reduce the likelihood ofincorporating such occasional mistakes into future re-training. It alsohelps adjust the algorithm for the future so that if user patternschange, the algorithm is self-adjusting to accommodate the new patterns.

With general reference to notations and nomenclature used herein,portions of the detailed description that follows may be presented interms of program procedures executed by a processor of a machine or ofmultiple networked machines. These procedural descriptions andrepresentations are used by those skilled in the art to most effectivelyconvey the substance of their work to others skilled in the art. Aprocedure is here, and generally, conceived to be a self-consistentsequence of operations leading to a desired result. These operations arethose requiring physical manipulations of physical quantities. Usually,though not necessarily, these quantities take the form of electrical,magnetic or optical communications capable of being stored, transferred,combined, compared, and otherwise manipulated. It proves convenient attimes, principally for reasons of common usage, to refer to what iscommunicated as bits, values, elements, symbols, characters, terms,numbers, or the like. It should be noted, however, that all of these andsimilar terms are to be associated with the appropriate physicalquantities and are merely convenient labels applied to those quantities.

Further, these manipulations are often referred to in terms, such asadding or comparing, which are commonly associated with mentaloperations performed by a human operator. However, no such capability ofa human operator is necessary, or desirable in most cases, in any of theoperations described herein that form part of one or more embodiments.Rather, these operations are machine operations. Useful machines forperforming operations of various embodiments include machinesselectively activated or configured by a routine stored within that iswritten in accordance with the teachings herein, and/or includeapparatus specially constructed for the required purpose. Variousembodiments also relate to apparatus or systems for performing theseoperations. These apparatus may be specially constructed for therequired purpose or may include a general purpose computer. The requiredstructure for a variety of these machines will appear from thedescription given.

Reference is now made to the drawings, wherein like reference numeralsare used to refer to like elements throughout. In the followingdescription, for purposes of explanation, numerous specific details areset forth in order to provide a thorough understanding thereof. It maybe evident, however, that the novel embodiments can be practiced withoutthese specific details. In other instances, well known structures anddevices are shown in block diagram form in order to facilitate adescription thereof. The intention is to cover all modifications,equivalents, and alternatives within the scope of the claims.

Systems depicted in some of the figures may be provided in variousconfigurations. In some embodiments, the systems may be configured as adistributed system where one or more components of the system aredistributed across one or more networks in a cloud computing systemand/or a fog computing system.

FIG. 1 is a block diagram that provides an illustration of the hardwarecomponents of a data transmission network 100, according to embodimentsof the present technology. Data transmission network 100 is aspecialized computer system that may be used for processing largeamounts of data where a large number of computer processing cycles arerequired.

Data transmission network 100 may also include computing environment114. Computing environment 114 may be a specialized computer or othermachine that processes the data received within the data transmissionnetwork 100. Data transmission network 100 also includes one or morenetwork devices 102. Network devices 102 may include client devices thatattempt to communicate with computing environment 114. For example,network devices 102 may send data to the computing environment 114 to beprocessed, may send signals to the computing environment 114 to controldifferent aspects of the computing environment or the data it isprocessing, among other reasons. Network devices 102 may interact withthe computing environment 114 through a number of ways, such as, forexample, over one or more networks 108. As shown in FIG. 1, computingenvironment 114 may include one or more other systems. For example,computing environment 114 may include a database system 118 and/or acommunications grid 120.

In other embodiments, network devices may provide a large amount ofdata, either all at once or streaming over a period of time (e.g., usingevent stream processing (ESP), described further with respect to FIGS.8-10), to the computing environment 114 via networks 108. For example,network devices 102 may include network computers, sensors, databases,or other devices that may transmit or otherwise provide data tocomputing environment 114. For example, network devices may includelocal area network devices, such as routers, hubs, switches, or othercomputer networking devices. These devices may provide a variety ofstored or generated data, such as network data or data specific to thenetwork devices themselves. Network devices may also include sensorsthat monitor their environment or other devices to collect dataregarding that environment or those devices, and such network devicesmay provide data they collect over time. Network devices may alsoinclude devices within the internet of things, such as devices within ahome automation network. Some of these devices may be referred to asedge devices, and may involve edge computing circuitry. Data may betransmitted by network devices directly to computing environment 114 orto network-attached data stores, such as network-attached data stores110 for storage so that the data may be retrieved later by the computingenvironment 114 or other portions of data transmission network 100.

Data transmission network 100 may also include one or morenetwork-attached data stores 110. Network-attached data stores 110 areused to store data to be processed by the computing environment 114 aswell as any intermediate or final data generated by the computing systemin non-volatile memory. However in certain embodiments, theconfiguration of the computing environment 114 allows its operations tobe performed such that intermediate and final data results can be storedsolely in volatile memory (e.g., RAM), without a requirement thatintermediate or final data results be stored to non-volatile types ofmemory (e.g., disk). This can be useful in certain situations, such aswhen the computing environment 114 receives ad hoc queries from a userand when responses, which are generated by processing large amounts ofdata, need to be generated on-the-fly. In this non-limiting situation,the computing environment 114 may be configured to retain the processedinformation within memory so that responses can be generated for theuser at different levels of detail as well as allow a user tointeractively query against this information.

Network-attached data stores may store a variety of different types ofdata organized in a variety of different ways and from a variety ofdifferent sources. For example, network-attached data storage mayinclude storage other than primary storage located within computingenvironment 114 that is directly accessible by processors locatedtherein. Network-attached data storage may include secondary, tertiaryor auxiliary storage, such as large hard drives, servers, virtualmemory, among other types. Storage devices may include portable ornon-portable storage devices, optical storage devices, and various othermediums capable of storing, containing data. A machine-readable storagemedium or computer-readable storage medium may include a non-transitorymedium in which data can be stored and that does not include carrierwaves and/or transitory electronic signals. Examples of a non-transitorymedium may include, for example, a magnetic disk or tape, opticalstorage media such as compact disk or digital versatile disk, flashmemory, memory or memory devices. A computer-program product may includecode and/or machine-executable instructions that may represent aprocedure, a function, a subprogram, a program, a routine, a subroutine,a module, a software package, a class, or any combination ofinstructions, data structures, or program statements. A code segment maybe coupled to another code segment or a hardware circuit by passingand/or receiving information, data, arguments, parameters, or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, amongothers. Furthermore, the data stores may hold a variety of differenttypes of data. For example, network-attached data stores 110 may holdunstructured (e.g., raw) data, such as manufacturing data (e.g., adatabase containing records identifying products being manufactured withparameter data for each product, such as colors and models) or productsales databases (e.g., a database containing individual data recordsidentifying details of individual product sales).

The unstructured data may be presented to the computing environment 114in different forms such as a flat file or a conglomerate of datarecords, and may have data values and accompanying time stamps. Thecomputing environment 114 may be used to analyze the unstructured datain a variety of ways to determine the best way to structure (e.g.,hierarchically) that data, such that the structured data is tailored toa type of further analysis that a user wishes to perform on the data.For example, after being processed, the unstructured time stamped datamay be aggregated by time (e.g., into daily time period units) togenerate time series data and/or structured hierarchically according toone or more dimensions (e.g., parameters, attributes, and/or variables).For example, data may be stored in a hierarchical data structure, suchas a ROLAP OR MOLAP database, or may be stored in another tabular form,such as in a flat-hierarchy form.

Data transmission network 100 may also include one or more server farms106. Computing environment 114 may route select communications or datato the one or more sever farms 106 or one or more servers within theserver farms. Server farms 106 can be configured to provide informationin a predetermined manner. For example, server farms 106 may access datato transmit in response to a communication. Server farms 106 may beseparately housed from each other device within data transmissionnetwork 100, such as computing environment 114, and/or may be part of adevice or system.

Server farms 106 may host a variety of different types of dataprocessing as part of data transmission network 100. Server farms 106may receive a variety of different data from network devices, fromcomputing environment 114, from cloud network 116, or from othersources. The data may have been obtained or collected from one or moresensors, as inputs from a control database, or may have been received asinputs from an external system or device. Server farms 106 may assist inprocessing the data by turning raw data into processed data based on oneor more rules implemented by the server farms. For example, sensor datamay be analyzed to determine changes in an environment over time or inreal-time.

Data transmission network 100 may also include one or more cloudnetworks 116. Cloud network 116 may include a cloud infrastructuresystem that provides cloud services. In certain embodiments, servicesprovided by the cloud network 116 may include a host of services thatare made available to users of the cloud infrastructure system on demandCloud network 116 is shown in FIG. 1 as being connected to computingenvironment 114 (and therefore having computing environment 114 as itsclient or user), but cloud network 116 may be connected to or utilizedby any of the devices in FIG. 1. Services provided by the cloud networkcan dynamically scale to meet the needs of its users. The cloud network116 may comprise one or more computers, servers, and/or systems. In someembodiments, the computers, servers, and/or systems that make up thecloud network 116 are different from the user's own on-premisescomputers, servers, and/or systems. For example, the cloud network 116may host an application, and a user may, via a communication networksuch as the Internet, on demand, order and use the application.

While each device, server and system in FIG. 1 is shown as a singledevice, it will be appreciated that multiple devices may instead beused. For example, a set of network devices can be used to transmitvarious communications from a single user, or remote server 140 mayinclude a server stack. As another example, data may be processed aspart of computing environment 114.

Each communication within data transmission network 100 (e.g., betweenclient devices, between servers 106 and computing environment 114 orbetween a server and a device) may occur over one or more networks 108.Networks 108 may include one or more of a variety of different types ofnetworks, including a wireless network, a wired network, or acombination of a wired and wireless network. Examples of suitablenetworks include the Internet, a personal area network, a local areanetwork (LAN), a wide area network (WAN), or a wireless local areanetwork (WLAN). A wireless network may include a wireless interface orcombination of wireless interfaces. As an example, a network in the oneor more networks 108 may include a short-range communication channel,such as a BLUETOOTH® communication channel or a BLUETOOTH® Low Energycommunication channel. A wired network may include a wired interface.The wired and/or wireless networks may be implemented using routers,access points, bridges, gateways, or the like, to connect devices in thenetwork 114, as will be further described with respect to FIG. 2. Theone or more networks 108 can be incorporated entirely within or caninclude an intranet, an extranet, or a combination thereof. In oneembodiment, communications between two or more systems and/or devicescan be achieved by a secure communications protocol, such as securesockets layer (SSL) or transport layer security (TLS). In addition, dataand/or transactional details may be encrypted.

Some aspects may utilize the Internet of Things (IoT), where things(e.g., machines, devices, phones, sensors) can be connected to networksand the data from these things can be collected and processed within thethings and/or external to the things. For example, the IoT can includesensors in many different devices, and high value analytics can beapplied to identify hidden relationships and drive increasedefficiencies. This can apply to both big data analytics and real-time(e.g., ESP) analytics. This will be described further below with respectto FIG. 2.

As noted, computing environment 114 may include a communications grid120 and a transmission network database system 118. Communications grid120 may be a grid-based computing system for processing large amounts ofdata. The transmission network database system 118 may be for managing,storing, and retrieving large amounts of data that are distributed toand stored in the one or more network-attached data stores 110 or otherdata stores that reside at different locations within the transmissionnetwork database system 118. The compute nodes in the grid-basedcomputing system 120 and the transmission network database system 118may share the same processor hardware, such as processors that arelocated within computing environment 114.

FIG. 2 illustrates an example network including an example set ofdevices communicating with each other over an exchange system and via anetwork, according to embodiments of the present technology. As noted,each communication within data transmission network 100 may occur overone or more networks. System 200 includes a network device 204configured to communicate with a variety of types of client devices, forexample client devices 230, over a variety of types of communicationchannels.

As shown in FIG. 2, network device 204 can transmit a communication overa network (e.g., a cellular network via a base station 210). Thecommunication can be routed to another network device, such as networkdevices 205-209, via base station 210. The communication can also berouted to computing environment 214 via base station 210. For example,network device 204 may collect data either from its surroundingenvironment or from other network devices (such as network devices205-209) and transmit that data to computing environment 214.

Although network devices 204-209 are shown in FIG. 2 as a mobile phone,laptop computer, tablet computer, temperature sensor, motion sensor, andaudio sensor respectively, the network devices may be or include sensorsthat are sensitive to detecting aspects of their environment. Forexample, the network devices may include sensors such as water sensors,power sensors, electrical current sensors, chemical sensors, opticalsensors, pressure sensors, geographic or position sensors (e.g., GPS),velocity sensors, acceleration sensors, flow rate sensors, among others.Examples of characteristics that may be sensed include force, torque,load, strain, position, temperature, air pressure, fluid flow, chemicalproperties, resistance, electromagnetic fields, radiation, irradiance,proximity, acoustics, moisture, distance, speed, vibrations,acceleration, electrical potential, electrical current, among others.The sensors may be mounted to various components used as part of avariety of different types of systems (e.g., an oil drilling operation).The network devices may detect and record data related to theenvironment that it monitors, and transmit that data to computingenvironment 214.

As noted, one type of system that may include various sensors thatcollect data to be processed and/or transmitted to a computingenvironment according to certain embodiments includes an oil drillingsystem. For example, the one or more drilling operation sensors mayinclude surface sensors that measure a hook load, a fluid rate, atemperature and a density in and out of the wellbore, a standpipepressure, a surface torque, a rotation speed of a drill pipe, a rate ofpenetration, a mechanical specific energy, etc. and downhole sensorsthat measure a rotation speed of a bit, fluid densities, downholetorque, downhole vibration (axial, tangential, lateral), a weightapplied at a drill bit, an annular pressure, a differential pressure, anazimuth, an inclination, a dog leg severity, a measured depth, avertical depth, a downhole temperature, etc. Besides the raw datacollected directly by the sensors, other data may include parameterseither developed by the sensors or assigned to the system by a client orother controlling device. For example, one or more drilling operationcontrol parameters may control settings such as a mud motor speed toflow ratio, a bit diameter, a predicted formation top, seismic data,weather data, etc. Other data may be generated using physical modelssuch as an earth model, a weather model, a seismic model, a bottom holeassembly model, a well plan model, an annular friction model, etc. Inaddition to sensor and control settings, predicted outputs, of forexample, the rate of penetration, mechanical specific energy, hook load,flow in fluid rate, flow out fluid rate, pump pressure, surface torque,rotation speed of the drill pipe, annular pressure, annular frictionpressure, annular temperature, equivalent circulating density, etc. mayalso be stored in the data warehouse.

In another example, another type of system that may include varioussensors that collect data to be processed and/or transmitted to acomputing environment according to certain embodiments includes a homeautomation or similar automated network in a different environment, suchas an office space, school, public space, sports venue, or a variety ofother locations. Network devices in such an automated network mayinclude network devices that allow a user to access, control, and/orconfigure various home appliances located within the user's home (e.g.,a television, radio, light, fan, humidifier, sensor, microwave, iron,and/or the like), or outside of the user's home (e.g., exterior motionsensors, exterior lighting, garage door openers, sprinkler systems, orthe like). For example, network device 102 may include a home automationswitch that may be coupled with a home appliance. In another embodiment,a network device can allow a user to access, control, and/or configuredevices, such as office-related devices (e.g., copy machine, printer, orfax machine), audio and/or video related devices (e.g., a receiver, aspeaker, a projector, a DVD player, or a television), media-playbackdevices (e.g., a compact disc player, a CD player, or the like),computing devices (e.g., a home computer, a laptop computer, a tablet, apersonal digital assistant (PDA), a computing device, or a wearabledevice), lighting devices (e.g., a lamp or recessed lighting), devicesassociated with a security system, devices associated with an alarmsystem, devices that can be operated in an automobile (e.g., radiodevices, navigation devices), and/or the like. Data may be collectedfrom such various sensors in raw form, or data may be processed by thesensors to create parameters or other data either developed by thesensors based on the raw data or assigned to the system by a client orother controlling device.

In another example, another type of system that may include varioussensors that collect data to be processed and/or transmitted to acomputing environment according to certain embodiments includes a poweror energy grid. A variety of different network devices may be includedin an energy grid, such as various devices within one or more powerplants, energy farms (e.g., wind farm, solar farm, among others) energystorage facilities, factories, homes and businesses of consumers, amongothers. One or more of such devices may include one or more sensors thatdetect energy gain or loss, electrical input or output or loss, and avariety of other efficiencies. These sensors may collect data to informusers of how the energy grid, and individual devices within the grid,may be functioning and how they may be made more efficient.

Network device sensors may also perform processing on data it collectsbefore transmitting the data to the computing environment 114, or beforedeciding whether to transmit data to the computing environment 114. Forexample, network devices may determine whether data collected meetscertain rules, for example by comparing data or values calculated fromthe data and comparing that data to one or more thresholds. The networkdevice may use this data and/or comparisons to determine if the datashould be transmitted to the computing environment 214 for further useor processing.

Computing environment 214 may include machines 220 and 240. Althoughcomputing environment 214 is shown in FIG. 2 as having two machines, 220and 240, computing environment 214 may have only one machine or may havemore than two machines. The machines that make up computing environment214 may include specialized computers, servers, or other machines thatare configured to individually and/or collectively process large amountsof data. The computing environment 214 may also include storage devicesthat include one or more databases of structured data, such as dataorganized in one or more hierarchies, or unstructured data. Thedatabases may communicate with the processing devices within computingenvironment 214 to distribute data to them. Since network devices maytransmit data to computing environment 214, that data may be received bythe computing environment 214 and subsequently stored within thosestorage devices. Data used by computing environment 214 may also bestored in data stores 235, which may also be a part of or connected tocomputing environment 214.

Computing environment 214 can communicate with various devices via oneor more routers 225 or other inter-network or intra-network connectioncomponents. For example, computing environment 214 may communicate withdevices 230 via one or more routers 225. Computing environment 214 maycollect, analyze and/or store data from or pertaining to communications,client device operations, client rules, and/or user-associated actionsstored at one or more data stores 235. Such data may influencecommunication routing to the devices within computing environment 214,how data is stored or processed within computing environment 214, amongother actions.

Notably, various other devices can further be used to influencecommunication routing and/or processing between devices within computingenvironment 214 and with devices outside of computing environment 214.For example, as shown in FIG. 2, computing environment 214 may include aweb server 240. Thus, computing environment 214 can retrieve data ofinterest, such as client information (e.g., product information, clientrules, etc.), technical product details, news, current or predictedweather, and so on.

In addition to computing environment 214 collecting data (e.g., asreceived from network devices, such as sensors, and client devices orother sources) to be processed as part of a big data analytics project,it may also receive data in real time as part of a streaming analyticsenvironment. As noted, data may be collected using a variety of sourcesas communicated via different kinds of networks or locally. Such datamay be received on a real-time streaming basis. For example, networkdevices may receive data periodically from network device sensors as thesensors continuously sense, monitor and track changes in theirenvironments. Devices within computing environment 214 may also performpre-analysis on data it receives to determine if the data receivedshould be processed as part of an ongoing project. The data received andcollected by computing environment 214, no matter what the source ormethod or timing of receipt, may be processed over a period of time fora client to determine results data based on the client's needs andrules.

FIG. 3 illustrates a representation of a conceptual model of acommunications protocol system, according to embodiments of the presenttechnology. More specifically, FIG. 3 identifies operation of acomputing environment in an Open Systems Interaction model thatcorresponds to various connection components. The model 300 shows, forexample, how a computing environment, such as computing environment 314(or computing environment 214 in FIG. 2) may communicate with otherdevices in its network, and control how communications between thecomputing environment and other devices are executed and under whatconditions.

The model can include layers 301-307. The layers are arranged in astack. Each layer in the stack serves the layer one level higher than it(except for the application layer, which is the highest layer), and isserved by the layer one level below it (except for the physical layer,which is the lowest layer). The physical layer is the lowest layerbecause it receives and transmits raw bites of data, and is the farthestlayer from the user in a communications system. On the other hand, theapplication layer is the highest layer because it interacts directlywith a software application.

As noted, the model includes a physical layer 301. Physical layer 301represents physical communication, and can define parameters of thatphysical communication. For example, such physical communication maycome in the form of electrical, optical, or electromagnetic signals.Physical layer 301 also defines protocols that may controlcommunications within a data transmission network.

Link layer 302 defines links and mechanisms used to transmit (i.e.,move) data across a network. The link layer 302 manages node-to-nodecommunications, such as within a grid computing environment. Link layer302 can detect and correct errors (e.g., transmission errors in thephysical layer 301). Link layer 302 can also include a media accesscontrol (MAC) layer and logical link control (LLC) layer.

Network layer 303 defines the protocol for routing within a network. Inother words, the network layer coordinates transferring data acrossnodes in a same network (e.g., such as a grid computing environment).Network layer 303 can also define the processes used to structure localaddressing within the network.

Transport layer 304 can manage the transmission of data and the qualityof the transmission and/or receipt of that data. Transport layer 304 canprovide a protocol for transferring data, such as, for example, aTransmission Control Protocol (TCP). Transport layer 304 can assembleand disassemble data frames for transmission. The transport layer canalso detect transmission errors occurring in the layers below it.

Session layer 305 can establish, maintain, and manage communicationconnections between devices on a network. In other words, the sessionlayer controls the dialogues or nature of communications between networkdevices on the network. The session layer may also establishcheckpointing, adjournment, termination, and restart procedures.

Presentation layer 306 can provide translation for communicationsbetween the application and network layers. In other words, this layermay encrypt, decrypt and/or format data based on data types and/orencodings known to be accepted by an application or network layer.

Application layer 307 interacts directly with software applications andend users, and manages communications between them. Application layer307 can identify destinations, local resource states or availabilityand/or communication content or formatting using the applications.

Intra-network connection components 321 and 322 are shown to operate inlower levels, such as physical layer 301 and link layer 302,respectively. For example, a hub can operate in the physical layer, aswitch can operate in the link layer, and a router can operate in thenetwork layer. Inter-network connection components 323 and 328 are shownto operate on higher levels, such as layers 303-307. For example,routers can operate in the network layer and network devices can operatein the transport, session, presentation, and application layers.

As noted, a computing environment 314 can interact with and/or operateon, in various embodiments, one, more, all or any of the various layers.For example, computing environment 314 can interact with a hub (e.g.,via the link layer) so as to adjust which devices the hub communicateswith. The physical layer may be served by the link layer, so it mayimplement such data from the link layer. For example, the computingenvironment 314 may control which devices it will receive data from. Forexample, if the computing environment 314 knows that a certain networkdevice has turned off, broken, or otherwise become unavailable orunreliable, the computing environment 314 may instruct the hub toprevent any data from being transmitted to the computing environment 314from that network device. Such a process may be beneficial to avoidreceiving data that is inaccurate or that has been influenced by anuncontrolled environment. As another example, computing environment 314can communicate with a bridge, switch, router or gateway and influencewhich device within the system (e.g., system 200) the component selectsas a destination. In some embodiments, computing environment 314 caninteract with various layers by exchanging communications with equipmentoperating on a particular layer by routing or modifying existingcommunications. In another embodiment, such as in a grid computingenvironment, a node may determine how data within the environment shouldbe routed (e.g., which node should receive certain data) based oncertain parameters or information provided by other layers within themodel.

As noted, the computing environment 314 may be a part of acommunications grid environment, the communications of which may beimplemented as shown in the protocol of FIG. 3. For example, referringback to FIG. 2, one or more of machines 220 and 240 may be part of acommunications grid computing environment. A gridded computingenvironment may be employed in a distributed system with non-interactiveworkloads where data resides in memory on the machines, or computenodes. In such an environment, analytic code, instead of a databasemanagement system, controls the processing performed by the nodes. Datais co-located by pre-distributing it to the grid nodes, and the analyticcode on each node loads the local data into memory. Each node may beassigned a particular task such as a portion of a processing project, orto organize or control other nodes within the grid.

FIG. 4 illustrates a communications grid computing system 400 includinga variety of control and worker nodes, according to embodiments of thepresent technology.

Communications grid computing system 400 includes three control nodesand one or more worker nodes. Communications grid computing system 400includes control nodes 402, 404, and 406. The control nodes arecommunicatively connected via communication paths 451, 453, and 455.Therefore, the control nodes may transmit information (e.g., related tothe communications grid or notifications), to and receive informationfrom each other. Although communications grid computing system 400 isshown in FIG. 4 as including three control nodes, the communicationsgrid may include more or less than three control nodes.

Communications grid computing system (or just “communications grid”) 400also includes one or more worker nodes. Shown in FIG. 4 are six workernodes 410-420. Although FIG. 4 shows six worker nodes, a communicationsgrid according to embodiments of the present technology may include moreor less than six worker nodes. The number of worker nodes included in acommunications grid may be dependent upon how large the project or dataset is being processed by the communications grid, the capacity of eachworker node, the time designated for the communications grid to completethe project, among others. Each worker node within the communicationsgrid 400 may be connected (wired or wirelessly, and directly orindirectly) to control nodes 402-406. Therefore, each worker node mayreceive information from the control nodes (e.g., an instruction toperform work on a project) and may transmit information to the controlnodes (e.g., a result from work performed on a project). Furthermore,worker nodes may communicate with each other (either directly orindirectly). For example, worker nodes may transmit data between eachother related to a job being performed or an individual task within ajob being performed by that worker node. However, in certainembodiments, worker nodes may not, for example, be connected(communicatively or otherwise) to certain other worker nodes. In anembodiment, worker nodes may only be able to communicate with thecontrol node that controls it, and may not be able to communicate withother worker nodes in the communications grid, whether they are otherworker nodes controlled by the control node that controls the workernode, or worker nodes that are controlled by other control nodes in thecommunications grid.

A control node may connect with an external device with which thecontrol node may communicate (e.g., a grid user, such as a server orcomputer, may connect to a controller of the grid). For example, aserver or computer may connect to control nodes and may transmit aproject or job to the node. The project may include a data set. The dataset may be of any size. Once the control node receives such a projectincluding a large data set, the control node may distribute the data setor projects related to the data set to be performed by worker nodes.Alternatively, for a project including a large data set, the data setmay be received or stored by a machine other than a control node (e.g.,a HADOOP® standard-compliant data node employing the HADOOP® DistributedFile System, or HDFS).

Control nodes may maintain knowledge of the status of the nodes in thegrid (i.e., grid status information), accept work requests from clients,subdivide the work across worker nodes, coordinate the worker nodes,among other responsibilities. Worker nodes may accept work requests froma control node and provide the control node with results of the workperformed by the worker node. A grid may be started from a single node(e.g., a machine, computer, server, etc.). This first node may beassigned or may start as the primary control node that will control anyadditional nodes that enter the grid.

When a project is submitted for execution (e.g., by a client or acontroller of the grid) it may be assigned to a set of nodes. After thenodes are assigned to a project, a data structure (i.e., a communicator)may be created. The communicator may be used by the project forinformation to be shared between the project code running on each node.A communication handle may be created on each node. A handle, forexample, is a reference to the communicator that is valid within asingle process on a single node, and the handle may be used whenrequesting communications between nodes.

A control node, such as control node 402, may be designated as theprimary control node. A server, computer or other external device mayconnect to the primary control node. Once the control node receives aproject, the primary control node may distribute portions of the projectto its worker nodes for execution. For example, when a project isinitiated on communications grid 400, primary control node 402 controlsthe work to be performed for the project in order to complete theproject as requested or instructed. The primary control node maydistribute work to the worker nodes based on various factors, such aswhich subsets or portions of projects may be completed most efficientlyand in the correct amount of time. For example, a worker node mayperform analysis on a portion of data that is already local (e.g.,stored on) the worker node. The primary control node also coordinatesand processes the results of the work performed by each worker nodeafter each worker node executes and completes its job. For example, theprimary control node may receive a result from one or more worker nodes,and the control node may organize (e.g., collect and assemble) theresults received and compile them to produce a complete result for theproject received from the end user.

Any remaining control nodes, such as control nodes 404 and 406, may beassigned as backup control nodes for the project. In an embodiment,backup control nodes may not control any portion of the project.Instead, backup control nodes may serve as a backup for the primarycontrol node and take over as primary control node if the primarycontrol node were to fail. If a communications grid were to include onlya single control node, and the control node were to fail (e.g., thecontrol node is shut off or breaks) then the communications grid as awhole may fail and any project or job being run on the communicationsgrid may fail and may not complete. While the project may be run again,such a failure may cause a delay (severe delay in some cases, such asovernight delay) in completion of the project. Therefore, a grid withmultiple control nodes, including a backup control node, may bebeneficial.

To add another node or machine to the grid, the primary control node mayopen a pair of listening sockets, for example. A socket may be used toaccept work requests from clients, and the second socket may be used toaccept connections from other grid nodes. The primary control node maybe provided with a list of other nodes (e.g., other machines, computers,servers) that will participate in the grid, and the role that each nodewill fill in the grid. Upon startup of the primary control node (e.g.,the first node on the grid), the primary control node may use a networkprotocol to start the server process on every other node in the grid.Command line parameters, for example, may inform each node of one ormore pieces of information, such as: the role that the node will have inthe grid, the host name of the primary control node, the port number onwhich the primary control node is accepting connections from peer nodes,among others. The information may also be provided in a configurationfile, transmitted over a secure shell tunnel, recovered from aconfiguration server, among others. While the other machines in the gridmay not initially know about the configuration of the grid, thatinformation may also be sent to each other node by the primary controlnode. Updates of the grid information may also be subsequently sent tothose nodes.

For any control node other than the primary control node added to thegrid, the control node may open three sockets. The first socket mayaccept work requests from clients, the second socket may acceptconnections from other grid members, and the third socket may connect(e.g., permanently) to the primary control node. When a control node(e.g., primary control node) receives a connection from another controlnode, it first checks to see if the peer node is in the list ofconfigured nodes in the grid. If it is not on the list, the control nodemay clear the connection. If it is on the list, it may then attempt toauthenticate the connection. If authentication is successful, theauthenticating node may transmit information to its peer, such as theport number on which a node is listening for connections, the host nameof the node, information about how to authenticate the node, among otherinformation. When a node, such as the new control node, receivesinformation about another active node, it will check to see if italready has a connection to that other node. If it does not have aconnection to that node, it may then establish a connection to thatcontrol node.

Any worker node added to the grid may establish a connection to theprimary control node and any other control nodes on the grid. Afterestablishing the connection, it may authenticate itself to the grid(e.g., any control nodes, including both primary and backup, or a serveror user controlling the grid). After successful authentication, theworker node may accept configuration information from the control node.

When a node joins a communications grid (e.g., when the node is poweredon or connected to an existing node on the grid or both), the node isassigned (e.g., by an operating system of the grid) a universally uniqueidentifier (UUID). This unique identifier may help other nodes andexternal entities (devices, users, etc.) to identify the node anddistinguish it from other nodes. When a node is connected to the grid,the node may share its unique identifier with the other nodes in thegrid. Since each node may share its unique identifier, each node mayknow the unique identifier of every other node on the grid. Uniqueidentifiers may also designate a hierarchy of each of the nodes (e.g.,backup control nodes) within the grid. For example, the uniqueidentifiers of each of the backup control nodes may be stored in a listof backup control nodes to indicate an order in which the backup controlnodes will take over for a failed primary control node to become a newprimary control node. However, a hierarchy of nodes may also bedetermined using methods other than using the unique identifiers of thenodes. For example, the hierarchy may be predetermined, or may beassigned based on other predetermined factors.

The grid may add new machines at any time (e.g., initiated from anycontrol node). Upon adding a new node to the grid, the control node mayfirst add the new node to its table of grid nodes. The control node mayalso then notify every other control node about the new node. The nodesreceiving the notification may acknowledge that they have updated theirconfiguration information.

Primary control node 402 may, for example, transmit one or morecommunications to backup control nodes 404 and 406 (and, for example, toother control or worker nodes within the communications grid). Suchcommunications may sent periodically, at fixed time intervals, betweenknown fixed stages of the project's execution, among other protocols.The communications transmitted by primary control node 402 may be ofvaried types and may include a variety of types of information. Forexample, primary control node 402 may transmit snapshots (e.g., statusinformation) of the communications grid so that backup control node 404always has a recent snapshot of the communications grid. The snapshot orgrid status may include, for example, the structure of the grid(including, for example, the worker nodes in the grid, uniqueidentifiers of the nodes, or their relationships with the primarycontrol node) and the status of a project (including, for example, thestatus of each worker node's portion of the project). The snapshot mayalso include analysis or results received from worker nodes in thecommunications grid. The backup control nodes may receive and store thebackup data received from the primary control node. The backup controlnodes may transmit a request for such a snapshot (or other information)from the primary control node, or the primary control node may send suchinformation periodically to the backup control nodes.

As noted, the backup data may allow the backup control node to take overas primary control node if the primary control node fails withoutrequiring the grid to start the project over from scratch. If theprimary control node fails, the backup control node that will take overas primary control node may retrieve the most recent version of thesnapshot received from the primary control node and use the snapshot tocontinue the project from the stage of the project indicated by thebackup data. This may prevent failure of the project as a whole.

A backup control node may use various methods to determine that theprimary control node has failed. In one example of such a method, theprimary control node may transmit (e.g., periodically) a communicationto the backup control node that indicates that the primary control nodeis working and has not failed, such as a heartbeat communication. Thebackup control node may determine that the primary control node hasfailed if the backup control node has not received a heartbeatcommunication for a certain predetermined period of time. Alternatively,a backup control node may also receive a communication from the primarycontrol node itself (before it failed) or from a worker node that theprimary control node has failed, for example because the primary controlnode has failed to communicate with the worker node.

Different methods may be performed to determine which backup controlnode of a set of backup control nodes (e.g., backup control nodes 404and 406) will take over for failed primary control node 402 and becomethe new primary control node. For example, the new primary control nodemay be chosen based on a ranking or “hierarchy” of backup control nodesbased on their unique identifiers. In an alternative embodiment, abackup control node may be assigned to be the new primary control nodeby another device in the communications grid or from an external device(e.g., a system infrastructure or an end user, such as a server orcomputer, controlling the communications grid). In another alternativeembodiment, the backup control node that takes over as the new primarycontrol node may be designated based on bandwidth or other statisticsabout the communications grid.

A worker node within the communications grid may also fail. If a workernode fails, work being performed by the failed worker node may beredistributed amongst the operational worker nodes. In an alternativeembodiment, the primary control node may transmit a communication toeach of the operable worker nodes still on the communications grid thateach of the worker nodes should purposefully fail also. After each ofthe worker nodes fail, they may each retrieve their most recent savedcheckpoint of their status and re-start the project from that checkpointto minimize lost progress on the project being executed.

FIG. 5 illustrates a flow chart showing an example process 500 foradjusting a communications grid or a work project in a communicationsgrid after a failure of a node, according to embodiments of the presenttechnology. The process may include, for example, receiving grid statusinformation including a project status of a portion of a project beingexecuted by a node in the communications grid, as described in operation502. For example, a control node (e.g., a backup control node connectedto a primary control node and a worker node on a communications grid)may receive grid status information, where the grid status informationincludes a project status of the primary control node or a projectstatus of the worker node. The project status of the primary controlnode and the project status of the worker node may include a status ofone or more portions of a project being executed by the primary andworker nodes in the communications grid. The process may also includestoring the grid status information, as described in operation 504. Forexample, a control node (e.g., a backup control node) may store thereceived grid status information locally within the control node.Alternatively, the grid status information may be sent to another devicefor storage where the control node may have access to the information.

The process may also include receiving a failure communicationcorresponding to a node in the communications grid in operation 506. Forexample, a node may receive a failure communication including anindication that the primary control node has failed, prompting a backupcontrol node to take over for the primary control node. In analternative embodiment, a node may receive a failure that a worker nodehas failed, prompting a control node to reassign the work beingperformed by the worker node. The process may also include reassigning anode or a portion of the project being executed by the failed node, asdescribed in operation 508. For example, a control node may designatethe backup control node as a new primary control node based on thefailure communication upon receiving the failure communication. If thefailed node is a worker node, a control node may identify a projectstatus of the failed worker node using the snapshot of thecommunications grid, where the project status of the failed worker nodeincludes a status of a portion of the project being executed by thefailed worker node at the failure time.

The process may also include receiving updated grid status informationbased on the reassignment, as described in operation 510, andtransmitting a set of instructions based on the updated grid statusinformation to one or more nodes in the communications grid, asdescribed in operation 512. The updated grid status information mayinclude an updated project status of the primary control node or anupdated project status of the worker node. The updated information maybe transmitted to the other nodes in the grid to update their stalestored information.

FIG. 6 illustrates a portion of a communications grid computing system600 including a control node and a worker node, according to embodimentsof the present technology. Communications grid 600 computing systemincludes one control node (control node 602) and one worker node (workernode 610) for purposes of illustration, but may include more workerand/or control nodes. The control node 602 is communicatively connectedto worker node 610 via communication path 650. Therefore, control node602 may transmit information (e.g., related to the communications gridor notifications), to and receive information from worker node 610 viapath 650.

Similar to in FIG. 4, communications grid computing system (or just“communications grid”) 600 includes data processing nodes (control node602 and worker node 610). Nodes 602 and 610 comprise multi-core dataprocessors. Each node 602 and 610 includes a grid-enabled softwarecomponent (GESC) 620 that executes on the data processor associated withthat node and interfaces with buffer memory 622 also associated withthat node. Each node 602 and 610 includes a database management software(DBMS) 628 that executes on a database server (not shown) at controlnode 602 and on a database server (not shown) at worker node 610.

Each node also includes a data store 624. Data stores 624, similar tonetwork-attached data stores 110 in FIG. 1 and data stores 235 in FIG.2, are used to store data to be processed by the nodes in the computingenvironment. Data stores 624 may also store any intermediate or finaldata generated by the computing system after being processed, forexample in non-volatile memory. However in certain embodiments, theconfiguration of the grid computing environment allows its operations tobe performed such that intermediate and final data results can be storedsolely in volatile memory (e.g., RAM), without a requirement thatintermediate or final data results be stored to non-volatile types ofmemory. Storing such data in volatile memory may be useful in certainsituations, such as when the grid receives queries (e.g., ad hoc) from aclient and when responses, which are generated by processing largeamounts of data, need to be generated quickly or on-the-fly. In such asituation, the grid may be configured to retain the data within memoryso that responses can be generated at different levels of detail and sothat a client may interactively query against this information.

Each node also includes a user-defined function (UDF) 626. The UDFprovides a mechanism for the DMBS 628 to transfer data to or receivedata from the database stored in the data stores 624 that are managed bythe DBMS. For example, UDF 626 can be invoked by the DBMS to providedata to the GESC for processing. The UDF 626 may establish a socketconnection (not shown) with the GESC to transfer the data.Alternatively, the UDF 626 can transfer data to the GESC by writing datato shared memory accessible by both the UDF and the GESC.

The GESC 620 at the nodes 602 and 620 may be connected via a network,such as network 108 shown in FIG. 1. Therefore, nodes 602 and 620 cancommunicate with each other via the network using a predeterminedcommunication protocol such as, for example, the Message PassingInterface (MPI). Each GESC 620 can engage in point-to-pointcommunication with the GESC at another node or in collectivecommunication with multiple GESCs via the network. The GESC 620 at eachnode may contain identical (or nearly identical) software instructions.Each node may be capable of operating as either a control node or aworker node. The GESC at the control node 602 can communicate, over acommunication path 652, with a client device 630. More specifically,control node 602 may communicate with client application 632 hosted bythe client device 630 to receive queries and to respond to those queriesafter processing large amounts of data.

DMBS 628 may control the creation, maintenance, and use of database ordata structure (not shown) within a nodes 602 or 610. The database mayorganize data stored in data stores 624. The DMBS 628 at control node602 may accept requests for data and transfer the appropriate data forthe request. With such a process, collections of data may be distributedacross multiple physical locations. In this example, each node 602 and610 stores a portion of the total data managed by the management systemin its associated data store 624.

Furthermore, the DBMS may be responsible for protecting against dataloss using replication techniques. Replication includes providing abackup copy of data stored on one node on one or more other nodes.Therefore, if one node fails, the data from the failed node can berecovered from a replicated copy residing at another node. However, asdescribed herein with respect to FIG. 4, data or status information foreach node in the communications grid may also be shared with each nodeon the grid.

FIG. 7 illustrates a flow chart showing an example method 700 forexecuting a project within a grid computing system, according toembodiments of the present technology. As described with respect to FIG.6, the GESC at the control node may transmit data with a client device(e.g., client device 630) to receive queries for executing a project andto respond to those queries after large amounts of data have beenprocessed. The query may be transmitted to the control node, where thequery may include a request for executing a project, as described inoperation 702. The query can contain instructions on the type of dataanalysis to be performed in the project and whether the project shouldbe executed using the grid-based computing environment, as shown inoperation 704.

To initiate the project, the control node may determine if the queryrequests use of the grid-based computing environment to execute theproject. If the determination is no, then the control node initiatesexecution of the project in a solo environment (e.g., at the controlnode), as described in operation 710. If the determination is yes, thecontrol node may initiate execution of the project in the grid-basedcomputing environment, as described in operation 706. In such asituation, the request may include a requested configuration of thegrid. For example, the request may include a number of control nodes anda number of worker nodes to be used in the grid when executing theproject. After the project has been completed, the control node maytransmit results of the analysis yielded by the grid, as described inoperation 708. Whether the project is executed in a solo or grid-basedenvironment, the control node provides the results of the project, asdescribed in operation 712.

As noted with respect to FIG. 2, the computing environments describedherein may collect data (e.g., as received from network devices, such assensors, such as network devices 204-209 in FIG. 2, and client devicesor other sources) to be processed as part of a data analytics project,and data may be received in real time as part of a streaming analyticsenvironment (e.g., ESP). Data may be collected using a variety ofsources as communicated via different kinds of networks or locally, suchas on a real-time streaming basis. For example, network devices mayreceive data periodically from network device sensors as the sensorscontinuously sense, monitor and track changes in their environments.More specifically, an increasing number of distributed applicationsdevelop or produce continuously flowing data from distributed sources byapplying queries to the data before distributing the data togeographically distributed recipients. An event stream processing engine(ESPE) may continuously apply the queries to the data as it is receivedand determines which entities should receive the data. Client or otherdevices may also subscribe to the ESPE or other devices processing ESPdata so that they can receive data after processing, based on forexample the entities determined by the processing engine. For example,client devices 230 in FIG. 2 may subscribe to the ESPE in computingenvironment 214. In another example, event subscription devices 1024a-c, described further with respect to FIG. 10, may also subscribe tothe ESPE. The ESPE may determine or define how input data or eventstreams from network devices or other publishers (e.g., network devices204-209 in FIG. 2) are transformed into meaningful output data to beconsumed by subscribers, such as for example client devices 230 in FIG.2.

FIG. 8 illustrates a block diagram including components of an EventStream Processing Engine (ESPE), according to embodiments of the presenttechnology. ESPE 800 may include one or more projects 802. A project maybe described as a second-level container in an engine model managed byESPE 800 where a thread pool size for the project may be defined by auser. Each project of the one or more projects 802 may include one ormore continuous queries 804 that contain data flows, which are datatransformations of incoming event streams. The one or more continuousqueries 804 may include one or more source windows 806 and one or morederived windows 808.

The ESPE may receive streaming data over a period of time related tocertain events, such as events or other data sensed by one or morenetwork devices. The ESPE may perform operations associated withprocessing data created by the one or more devices. For example, theESPE may receive data from the one or more network devices 204-209 shownin FIG. 2. As noted, the network devices may include sensors that sensedifferent aspects of their environments, and may collect data over timebased on those sensed observations. For example, the ESPE may beimplemented within one or more of machines 220 and 240 shown in FIG. 2.The ESPE may be implemented within such a machine by an ESP application.An ESP application may embed an ESPE with its own dedicated thread poolor pools into its application space where the main application threadcan do application-specific work and the ESPE processes event streams atleast by creating an instance of a model into processing objects.

The engine container is the top-level container in a model that managesthe resources of the one or more projects 802. In an illustrativeembodiment, for example, there may be only one ESPE 800 for eachinstance of the ESP application, and ESPE 800 may have a unique enginename. Additionally, the one or more projects 802 may each have uniqueproject names, and each query may have a unique continuous query nameand begin with a uniquely named source window of the one or more sourcewindows 806. ESPE 800 may or may not be persistent.

Continuous query modeling involves defining directed graphs of windowsfor event stream manipulation and transformation. A window in thecontext of event stream manipulation and transformation is a processingnode in an event stream processing model. A window in a continuous querycan perform aggregations, computations, pattern-matching, and otheroperations on data flowing through the window. A continuous query may bedescribed as a directed graph of source, relational, pattern matching,and procedural windows. The one or more source windows 806 and the oneor more derived windows 808 represent continuously executing queriesthat generate updates to a query result set as new event blocks streamthrough ESPE 800. A directed graph, for example, is a set of nodesconnected by edges, where the edges have a direction associated withthem.

An event object may be described as a packet of data accessible as acollection of fields, with at least one of the fields defined as a keyor unique identifier (ID). The event object may be created using avariety of formats including binary, alphanumeric, XML, etc. Each eventobject may include one or more fields designated as a primary identifier(ID) for the event so ESPE 800 can support operation codes (opcodes) forevents including insert, update, upsert, and delete. Upsert opcodesupdate the event if the key field already exists; otherwise, the eventis inserted. For illustration, an event object may be a packed binaryrepresentation of a set of field values and include both metadata andfield data associated with an event. The metadata may include an opcodeindicating if the event represents an insert, update, delete, or upsert,a set of flags indicating if the event is a normal, partial-update, or aretention generated event from retention policy management, and a set ofmicrosecond timestamps that can be used for latency measurements.

An event block object may be described as a grouping or package of eventobjects. An event stream may be described as a flow of event blockobjects. A continuous query of the one or more continuous queries 804transforms a source event stream made up of streaming event blockobjects published into ESPE 800 into one or more output event streamsusing the one or more source windows 806 and the one or more derivedwindows 808. A continuous query can also be thought of as data flowmodeling.

The one or more source windows 806 are at the top of the directed graphand have no windows feeding into them. Event streams are published intothe one or more source windows 806, and from there, the event streamsmay be directed to the next set of connected windows as defined by thedirected graph. The one or more derived windows 808 are all instantiatedwindows that are not source windows and that have other windowsstreaming events into them. The one or more derived windows 808 mayperform computations or transformations on the incoming event streams.The one or more derived windows 808 transform event streams based on thewindow type (that is operators such as join, filter, compute, aggregate,copy, pattern match, procedural, union, etc.) and window settings. Asevent streams are published into ESPE 800, they are continuouslyqueried, and the resulting sets of derived windows in these queries arecontinuously updated.

FIG. 9 illustrates a flow chart showing an example process includingoperations performed by an event stream processing engine, according tosome embodiments of the present technology. As noted, the ESPE 800 (oran associated ESP application) defines how input event streams aretransformed into meaningful output event streams. More specifically, theESP application may define how input event streams from publishers(e.g., network devices providing sensed data) are transformed intomeaningful output event streams consumed by subscribers (e.g., a dataanalytics project being executed by a machine or set of machines).

Within the application, a user may interact with one or more userinterface windows presented to the user in a display under control ofthe ESPE independently or through a browser application in an orderselectable by the user. For example, a user may execute an ESPapplication, which causes presentation of a first user interface window,which may include a plurality of menus and selectors such as drop downmenus, buttons, text boxes, hyperlinks, etc. associated with the ESPapplication as understood by a person of skill in the art. As furtherunderstood by a person of skill in the art, various operations may beperformed in parallel, for example, using a plurality of threads.

At operation 900, an ESP application may define and start an ESPE,thereby instantiating an ESPE at a device, such as machine 220 and/or240. In an operation 902, the engine container is created. Forillustration, ESPE 800 may be instantiated using a function call thatspecifies the engine container as a manager for the model.

In an operation 904, the one or more continuous queries 804 areinstantiated by ESPE 800 as a model. The one or more continuous queries804 may be instantiated with a dedicated thread pool or pools thatgenerate updates as new events stream through ESPE 800. Forillustration, the one or more continuous queries 804 may be created tomodel business processing logic within ESPE 800, to predict eventswithin ESPE 800, to model a physical system within ESPE 800, to predictthe physical system state within ESPE 800, etc. For example, as noted,ESPE 800 may be used to support sensor data monitoring and management(e.g., sensing may include force, torque, load, strain, position,temperature, air pressure, fluid flow, chemical properties, resistance,electromagnetic fields, radiation, irradiance, proximity, acoustics,moisture, distance, speed, vibrations, acceleration, electricalpotential, or electrical current, etc.).

ESPE 800 may analyze and process events in motion or “event streams.”Instead of storing data and running queries against the stored data,ESPE 800 may store queries and stream data through them to allowcontinuous analysis of data as it is received. The one or more sourcewindows 806 and the one or more derived windows 808 may be created basedon the relational, pattern matching, and procedural algorithms thattransform the input event streams into the output event streams tomodel, simulate, score, test, predict, etc. based on the continuousquery model defined and application to the streamed data.

In an operation 906, a publish/subscribe (pub/sub) capability isinitialized for ESPE 800. In an illustrative embodiment, a pub/subcapability is initialized for each project of the one or more projects802. To initialize and enable pub/sub capability for ESPE 800, a portnumber may be provided. Pub/sub clients can use a host name of an ESPdevice running the ESPE and the port number to establish pub/subconnections to ESPE 800.

FIG. 10 illustrates an ESP system 1000 interfacing between publishingdevice 1022 and event subscribing devices 1024 a-c, according toembodiments of the present technology. ESP system 1000 may include ESPdevice or subsystem 851, event publishing device 1022, an eventsubscribing device A 1024 a, an event subscribing device B 1024 b, andan event subscribing device C 1024 c. Input event streams are output toESP device 851 by publishing device 1022. In alternative embodiments,the input event streams may be created by a plurality of publishingdevices. The plurality of publishing devices further may publish eventstreams to other ESP devices. The one or more continuous queriesinstantiated by ESPE 800 may analyze and process the input event streamsto form output event streams output to event subscribing device A 1024a, event subscribing device B 1024 b, and event subscribing device C1024 c. ESP system 1000 may include a greater or a fewer number of eventsubscribing devices of event subscribing devices.

Publish-subscribe is a message-oriented interaction paradigm based onindirect addressing. Processed data recipients specify their interest inreceiving information from ESPE 800 by subscribing to specific classesof events, while information sources publish events to ESPE 800 withoutdirectly addressing the receiving parties. ESPE 800 coordinates theinteractions and processes the data. In some cases, the data sourcereceives confirmation that the published information has been receivedby a data recipient.

A publish/subscribe API may be described as a library that enables anevent publisher, such as publishing device 1022, to publish eventstreams into ESPE 800 or an event subscriber, such as event subscribingdevice A 1024 a, event subscribing device B 1024 b, and eventsubscribing device C 1024 c, to subscribe to event streams from ESPE800. For illustration, one or more publish/subscribe APIs may bedefined. Using the publish/subscribe API, an event publishingapplication may publish event streams into a running event streamprocessor project source window of ESPE 800, and the event subscriptionapplication may subscribe to an event stream processor project sourcewindow of ESPE 800.

The publish/subscribe API provides cross-platform connectivity andendianness compatibility between ESP application and other networkedapplications, such as event publishing applications instantiated atpublishing device 1022, and event subscription applications instantiatedat one or more of event subscribing device A 1024 a, event subscribingdevice B 1024 b, and event subscribing device C 1024 c.

Referring back to FIG. 9, operation 906 initializes thepublish/subscribe capability of ESPE 800. In an operation 908, the oneor more projects 802 are started. The one or more started projects mayrun in the background on an ESP device. In an operation 910, an eventblock object is received from one or more computing device of the eventpublishing device 1022.

ESP subsystem 800 may include a publishing client 1002, ESPE 800, asubscribing client A 1004, a subscribing client B 1006, and asubscribing client C 1008. Publishing client 1002 may be started by anevent publishing application executing at publishing device 1022 usingthe publish/subscribe API. Subscribing client A 1004 may be started byan event subscription application A, executing at event subscribingdevice A 1024 a using the publish/subscribe API. Subscribing client B1006 may be started by an event subscription application B executing atevent subscribing device B 1024 b using the publish/subscribe API.Subscribing client C 1008 may be started by an event subscriptionapplication C executing at event subscribing device C 1024 c using thepublish/subscribe API.

An event block object containing one or more event objects is injectedinto a source window of the one or more source windows 806 from aninstance of an event publishing application on event publishing device1022. The event block object may generated, for example, by the eventpublishing application and may be received by publishing client 1002. Aunique ID may be maintained as the event block object is passed betweenthe one or more source windows 806 and/or the one or more derivedwindows 808 of ESPE 800, and to subscribing client A 1004, subscribingclient B 1006, and subscribing client C 1008 and to event subscriptiondevice A 1024 a, event subscription device B 1024 b, and eventsubscription device C 1024 c. Publishing client 1002 may furthergenerate and include a unique embedded transaction ID in the event blockobject as the event block object is processed by a continuous query, aswell as the unique ID that publishing device 1022 assigned to the eventblock object.

In an operation 912, the event block object is processed through the oneor more continuous queries 804. In an operation 914, the processed eventblock object is output to one or more computing devices of the eventsubscribing devices 1024 a-c. For example, subscribing client A 1004,subscribing client B 1006, and subscribing client C 1008 may send thereceived event block object to event subscription device A 1024 a, eventsubscription device B 1024 b, and event subscription device C 1024 c,respectively.

ESPE 800 maintains the event block containership aspect of the receivedevent blocks from when the event block is published into a source windowand works its way through the directed graph defined by the one or morecontinuous queries 804 with the various event translations before beingoutput to subscribers. Subscribers can correlate a group of subscribedevents back to a group of published events by comparing the unique ID ofthe event block object that a publisher, such as publishing device 1022,attached to the event block object with the event block ID received bythe subscriber.

In an operation 916, a determination is made concerning whether or notprocessing is stopped. If processing is not stopped, processingcontinues in operation 910 to continue receiving the one or more eventstreams containing event block objects from the, for example, one ormore network devices. If processing is stopped, processing continues inan operation 918. In operation 918, the started projects are stopped. Inoperation 920, the ESPE is shutdown.

As noted, in some embodiments, big data is processed for an analyticsproject after the data is received and stored. In other embodiments,distributed applications process continuously flowing data in real-timefrom distributed sources by applying queries to the data beforedistributing the data to geographically distributed recipients. Asnoted, an event stream processing engine (ESPE) may continuously applythe queries to the data as it is received and determines which entitiesreceive the processed data. This allows for large amounts of data beingreceived and/or collected in a variety of environments to be processedand distributed in real time. For example, as shown with respect to FIG.2, data may be collected from network devices that may include deviceswithin the internet of things, such as devices within a home automationnetwork. However, such data may be collected from a variety of differentresources in a variety of different environments. In any such situation,embodiments of the present technology allow for real-time processing ofsuch data.

Aspects of the current disclosure provide technical solutions totechnical problems, such as computing problems that arise when an ESPdevice fails which results in a complete service interruption andpotentially significant data loss. The data loss can be catastrophicwhen the streamed data is supporting mission critical operations such asthose in support of an ongoing manufacturing or drilling operation. Anembodiment of an ESP system achieves a rapid and seamless failover ofESPE running at the plurality of ESP devices without serviceinterruption or data loss, thus significantly improving the reliabilityof an operational system that relies on the live or real-time processingof the data streams. The event publishing systems, the event subscribingsystems, and each ESPE not executing at a failed ESP device are notaware of or effected by the failed ESP device. The ESP system mayinclude thousands of event publishing systems and event subscribingsystems. The ESP system keeps the failover logic and awareness withinthe boundaries of out-messaging network connector and out-messagingnetwork device.

In one example embodiment, a system is provided to support a failoverwhen event stream processing (ESP) event blocks. The system includes,but is not limited to, an out-messaging network device and a computingdevice. The computing device includes, but is not limited to, aprocessor and a computer-readable medium operably coupled to theprocessor. The processor is configured to execute an ESP engine (ESPE).The computer-readable medium has instructions stored thereon that, whenexecuted by the processor, cause the computing device to support thefailover. An event block object is received from the ESPE that includesa unique identifier. A first status of the computing device as active orstandby is determined. When the first status is active, a second statusof the computing device as newly active or not newly active isdetermined. Newly active is determined when the computing device isswitched from a standby status to an active status. When the secondstatus is newly active, a last published event block object identifierthat uniquely identifies a last published event block object isdetermined. A next event block object is selected from a non-transitorycomputer-readable medium accessible by the computing device. The nextevent block object has an event block object identifier that is greaterthan the determined last published event block object identifier. Theselected next event block object is published to an out-messagingnetwork device. When the second status of the computing device is notnewly active, the received event block object is published to theout-messaging network device. When the first status of the computingdevice is standby, the received event block object is stored in thenon-transitory computer-readable medium.

FIG. 11 is a flow chart of an example of a process for generating andusing a machine-learning model according to some aspects. Machinelearning is a branch of artificial intelligence that relates tomathematical models that can learn from, categorize, and makepredictions about data. Such mathematical models, which can be referredto as machine-learning models, can classify input data among two or moreclasses; cluster input data among two or more groups; predict a resultbased on input data; identify patterns or trends in input data; identifya distribution of input data in a space; or any combination of these.Examples of machine-learning models can include (i) neural networks;(ii) decision trees, such as classification trees and regression trees;(iii) classifiers, such as Naïve bias classifiers, logistic regressionclassifiers, ridge regression classifiers, random forest classifiers,least absolute shrinkage and selector (LASSO) classifiers, and supportvector machines; (iv) clusterers, such as k-means clusterers, mean-shiftclusterers, and spectral clusterers; (v) factorizers, such asfactorization machines, principal component analyzers and kernelprincipal component analyzers; and (vi) ensembles or other combinationsof machine-learning models. In some examples, neural networks caninclude deep neural networks, feed-forward neural networks, recurrentneural networks, convolutional neural networks, radial basis function(RBF) neural networks, echo state neural networks, long short-termmemory neural networks, bi-directional recurrent neural networks, gatedneural networks, hierarchical recurrent neural networks, stochasticneural networks, modular neural networks, spiking neural networks,dynamic neural networks, cascading neural networks, neuro-fuzzy neuralnetworks, or any combination of these.

Different machine-learning models may be used interchangeably to performa task. Examples of tasks that can be performed at least partially usingmachine-learning models include various types of scoring;bioinformatics; cheminformatics; software engineering; fraud detection;customer segmentation; generating online recommendations; adaptivewebsites; determining customer lifetime value; search engines; placingadvertisements in real time or near real time; classifying DNAsequences; affective computing; performing natural language processingand understanding; object recognition and computer vision; roboticlocomotion; playing games; optimization and metaheuristics; detectingnetwork intrusions; medical diagnosis and monitoring; or predicting whenan asset, such as a machine, will need maintenance.

Any number and combination of tools can be used to createmachine-learning models. Examples of tools for creating and managingmachine-learning models can include SAS® Enterprise Miner, SAS® RapidPredictive Modeler, and SAS® Model Manager, SAS Cloud Analytic Services(CAS)®, SAS Viya® of all which are by SAS Institute Inc. of Cary, N.C.

Machine-learning models can be constructed through an at least partiallyautomated (e.g., with little or no human involvement) process calledtraining. During training, input data can be iteratively supplied to amachine-learning model to enable the machine-learning model to identifypatterns related to the input data or to identify relationships betweenthe input data and output data. With training, the machine-learningmodel can be transformed from an untrained state to a trained state.Input data can be split into one or more training sets and one or morevalidation sets, and the training process may be repeated multipletimes. The splitting may follow a k-fold cross-validation rule, aleave-one-out-rule, a leave-p-out rule, or a holdout rule. An overviewof training and using a machine-learning model is described below withrespect to the flow chart of FIG. 11.

In block 1104, training data is received. In some examples, the trainingdata is received from a remote database or a local database, constructedfrom various subsets of data, or input by a user. The training data canbe used in its raw form for training a machine-learning model orpre-processed into another form, which can then be used for training themachine-learning model. For example, the raw form of the training datacan be smoothed, truncated, aggregated, clustered, or otherwisemanipulated into another form, which can then be used for training themachine-learning model.

In block 1106, a machine-learning model is trained using the trainingdata. The machine-learning model can be trained in a supervised,unsupervised, or semi-supervised manner. In supervised training, eachinput in the training data is correlated to a desired output. Thisdesired output may be a scalar, a vector, or a different type of datastructure such as text or an image. This may enable the machine-learningmodel to learn a mapping between the inputs and desired outputs. Inunsupervised training, the training data includes inputs, but notdesired outputs, so that the machine-learning model has to findstructure in the inputs on its own. In semi-supervised training, onlysome of the inputs in the training data are correlated to desiredoutputs.

In block 1108, the machine-learning model is evaluated. For example, anevaluation dataset can be obtained, for example, via user input or froma database. The evaluation dataset can include inputs correlated todesired outputs. The inputs can be provided to the machine-learningmodel and the outputs from the machine-learning model can be compared tothe desired outputs. If the outputs from the machine-learning modelclosely correspond with the desired outputs, the machine-learning modelmay have a high degree of accuracy. For example, if 90% or more of theoutputs from the machine-learning model are the same as the desiredoutputs in the evaluation dataset, the machine-learning model may have ahigh degree of accuracy. Otherwise, the machine-learning model may havea low degree of accuracy. The 90% number is an example only. A realisticand desirable accuracy percentage is dependent on the problem and thedata.

In some examples, if the machine-learning model has an inadequate degreeof accuracy for a particular task, the process can return to block 1106,where the machine-learning model can be further trained using additionaltraining data or otherwise modified to improve accuracy. If themachine-learning model has an adequate degree of accuracy for theparticular task, the process can continue to block 1110.

In block 1110, new data is received. In some examples, the new data isreceived from a remote database or a local database, constructed fromvarious subsets of data, or input by a user. The new data may be unknownto the machine-learning model. For example, the machine-learning modelmay not have previously processed or analyzed the new data.

In block 1112, the trained machine-learning model is used to analyze thenew data and provide a result. For example, the new data can be providedas input to the trained machine-learning model. The trainedmachine-learning model can analyze the new data and provide a resultthat includes a classification of the new data into a particular class,a clustering of the new data into a particular group, a prediction basedon the new data, or any combination of these.

In block 1114, the result is post-processed. For example, the result canbe added to, multiplied with, or otherwise combined with other data aspart of a job. As another example, the result can be transformed from afirst format, such as a time series format, into another format, such asa count series format. Any number and combination of operations can beperformed on the result during post-processing.

A more specific example of a machine-learning model is the neuralnetwork 1200 shown in FIG. 12. The neural network 1200 is represented asmultiple layers of interconnected neurons, such as neuron 1208, that canexchange data between one another. The layers include an input layer1202 for receiving input data, a hidden layer 1204, and an output layer1206 for providing a result. The hidden layer 1204 is referred to ashidden because it may not be directly observable or have its inputdirectly accessible during the normal functioning of the neural network1200. Although the neural network 1200 is shown as having a specificnumber of layers and neurons for exemplary purposes, the neural network1200 can have any number and combination of layers, and each layer canhave any number and combination of neurons.

The neurons and connections between the neurons can have numericweights, which can be tuned during training. For example, training datacan be provided to the input layer 1202 of the neural network 1200, andthe neural network 1200 can use the training data to tune one or morenumeric weights of the neural network 1200. In some examples, the neuralnetwork 1200 can be trained using backpropagation. Backpropagation caninclude determining a gradient of a particular numeric weight based on adifference between an actual output of the neural network 1200 and adesired output of the neural network 1200. Based on the gradient, one ormore numeric weights of the neural network 1200 can be updated to reducethe difference, thereby increasing the accuracy of the neural network1200. This process can be repeated multiple times to train the neuralnetwork 1200. For example, this process can be repeated hundreds orthousands of times to train the neural network 1200.

In some examples, the neural network 1200 is a feed-forward neuralnetwork. In a feed-forward neural network, every neuron only propagatesan output value to a subsequent layer of the neural network 1200. Forexample, data may only move one direction (forward) from one neuron tothe next neuron in a feed-forward neural network.

In other examples, the neural network 1200 is a recurrent neuralnetwork. A recurrent neural network can include one or more feedbackloops, allowing data to propagate in both forward and backward throughthe neural network 1200. This can allow for information to persistwithin the recurrent neural network. For example, a recurrent neuralnetwork can determine an output based at least partially on informationthat the recurrent neural network has seen before, giving the recurrentneural network the ability to use previous input to inform the output.

In some examples, the neural network 1200 operates by receiving a vectorof numbers from one layer; transforming the vector of numbers into a newvector of numbers using a matrix of numeric weights, a nonlinearity, orboth; and providing the new vector of numbers to a subsequent layer ofthe neural network 1200. Each subsequent layer of the neural network1200 can repeat this process until the neural network 1200 outputs afinal result at the output layer 1206. For example, the neural network1200 can receive a vector of numbers as an input at the input layer1202. The neural network 1200 can multiply the vector of numbers by amatrix of numeric weights to determine a weighted vector. The matrix ofnumeric weights can be tuned during the training of the neural network1200. The neural network 1200 can transform the weighted vector using anonlinearity, such as a sigmoid tangent or the hyperbolic tangent. Insome examples, the nonlinearity can include a rectified linear unit,which can be expressed using the equation y=max(x, 0) where y is theoutput and x is an input value from the weighted vector. The transformedoutput can be supplied to a subsequent layer, such as the hidden layer1204, of the neural network 1200. The subsequent layer of the neuralnetwork 1200 can receive the transformed output, multiply thetransformed output by a matrix of numeric weights and a nonlinearity,and provide the result to yet another layer of the neural network 1200.This process continues until the neural network 1200 outputs a finalresult at the output layer 1206.

Other examples of the present disclosure may include any number andcombination of machine-learning models having any number and combinationof characteristics. The machine-learning model(s) can be trained in asupervised, semi-supervised, or unsupervised manner, or any combinationof these. The machine-learning model(s) can be implemented using asingle computing device or multiple computing devices, such as thecommunications grid computing system 400 discussed above.

Implementing some examples of the present disclosure at least in part byusing machine-learning models can reduce the total number of processingiterations, time, memory, electrical power, or any combination of theseconsumed by a computing device when analyzing data. For example, aneural network may more readily identify patterns in data than otherapproaches. This may enable the neural network to analyze the data usingfewer processing cycles and less memory than other approaches, whileobtaining a similar or greater level of accuracy.

FIGS. 13A, 13B and 13C, together, illustrate an example embodiment of adistributed processing system 2000 incorporating one or more storagedevices 2100 that may form a storage device grid 2001, multiple nodedevices 2300 of node device grid 2003, a coordinating device 2500 and aviewing device 2700 coupled by a network 2999. FIG. 14 illustrates analternate example embodiment of the distributed online library system2000 in which the node devices 2300 may perform the functions of the oneor more storage devices 2100 such that the storage device grid 2001 maybe incorporated into the node device grid 2003. In both of theembodiments of FIGS. 13A-C and 14, the distributed processing system2000 performs data preparation operations on data sets 2130.Additionally, in both of these embodiments, the distributed processingsystem 2000 also suggests data preparation operations to be performed oneach data set 2130 based on its features to aid operators thereof inselecting the data preparation operations that are to be performed bythe distributed processing system 2000 on each data set 2130. Further,in both of these embodiments, the features of data sets 2130 that aredetected and the variety of data preparation operations about which suchsuggestions are made are extensible.

FIG. 15 illustrates aspects of the provision, exchange and use ofvarious pieces of data within the devices 2100, 2300, 2500 and 2700, andamong these devices via the network 2999. As will be explained ingreater detail, as part of performing the aforementioned suggestion anddata preparation operations, these devices may exchange a variety ofportions of data sets 2130 (e.g., data set portions 2131), metadata 2135and/or context data 2335, as well as training equivalents of each,including portions of training data sets 2110 (e.g., data set portions2111), training metadata 2115 and/or training context data 2315.Additionally, these devices may exchange sets of feature routines 2240and/or suggestion models 2470 as part of detecting data set featuresand/or deriving suggestions at least partially in parallel. In variousembodiments, the network 2999 may be a single network that may extendwithin a single building or other relatively limited area, a combinationof connected networks that may extend a considerable distance, and/ormay include the Internet. Thus, the network 2999 may be based on any ofa variety (or combination) of communications technologies by whichcommunications may be effected, including without limitation, wiredtechnologies employing electrically and/or optically conductive cabling,and wireless technologies employing infrared, radio frequency (RF) orother forms of wireless transmission.

Turning to FIGS. 13A-C, as well as to FIG. 15, in various embodiments,each of the storage devices 2100 may incorporate one or more of aprocessor 2150, a storage 2160 and a network interface 2190 to coupleeach of the storage devices 2100 to the network 2999. The storage 2160may store a control routine 2140, one or more data sets 2130, and/or oneor more training data sets 2110. The control routine 2140 mayincorporate a sequence of instructions operative on the processor 2150of each of the storage devices 2100 to implement logic to performvarious functions, at least partially in parallel with the processors2150 of others of the storage devices 2100. In executing the controlroutine 2140, the processor 2150 of each of the storage devices 2100 mayoperate the network interface 2190 thereof to receive data items of eachof one or more of the data sets 2130 via the network 2999, and may storesuch data items as part thereof. The processor 2150 of each of thestorage devices 2100 may also operate the network interface 2190 toprovide an indication to the coordinating device 2500 of theavailability of one or more of the data sets 2130 via the network 2999.Providing such an indication to the coordinating device 2500 for aparticular data set 2130 may be in response to having received all ofthe data items of that data set 2130 such that it is available from theone or more storage devices in complete form.

Each of the one or more data sets 2130 may include any of a wide varietyof types of data associated with any of a wide variety of subjects. Byway of example, each data set 2130 may include scientific observationdata concerning geological and/or meteorological events, or from sensorsemployed in laboratory experiments in areas such as particle physics. Byway of another example, each data set 2130 may include indications ofactivities performed by a random sample of individuals of a populationof people in a selected country or municipality, or of a population of athreatened species under study in the wild.

In some embodiments, the processors 2150 of the storage devices 1100 maycooperate to perform a collection function in which each of theprocessors 2150 operates a corresponding one of the network interfaces2190 to receive data items of one or more of the data sets 2130 via thenetwork 2999, and may assemble the received data items into the one ormore data sets 2130 over a period of time. In such embodiments, dataitems of a data set 2130 may be received via the network 2999 and/or inother ways from one or more other devices (not shown). By way ofexample, a multitude of remotely located sensor devices (e.g.,geological sensors dispersed about a particular geological region, orparticle detection sensors disposed at various portions of a particleaccelerator) may generate numerous data items that are then provided viathe network 2999 to the storage devices 2100 where the numerous dataitems are then assembled to form a data set 2130. In other embodiments,the storage devices 2100 may receive one or more of the data sets 2130from a multitude of other devices (not shown), such as another grid ofother node devices. By way of example, such other devices may performone or more processing operations that generates a data set 2130 (e.g.,use a Bayesian analysis to derive a prediction of the behavior of peoplein a simulation of evacuating a burning building, or to derive aprediction of behavior of structural components of a bridge in responseto various wind flows), and may then transmit a data set 2130 as anoutput to the storage device grid 2001.

The one or more training data sets 2110 are employed to prepare thedistributed processing system 2000 for normal use, including trainingthe ability of the system 2000 to suggest data preparation operations tobe performed on each data set 2130. The one or more training data sets2110 may include training data set(s) 2110 that incorporate simulateddata values that are randomly generated and/or may be generated toincorporate random, but known, combinations of features in support ofusing the training data sets 2110 for such training. Alternatively oradditionally, the one or more training data sets 2110 may includepreviously encountered ones of the one or more data sets 2130 that havebeen selected due to the combinations of features that have beenpreviously detected in each. Regardless of the exact manner in whicheach training data set 2110 is generated, as will be explained ingreater detail, the one or more training data sets 2110 may be providedto the system 2000 from an outside source (not shown) as part of aninitialization data 2933 used in initial preparation of the system 2000for use. Alternatively or additionally, the one or more training datasets 2100 may be similarly provided to the system 2000 from an outsidesource (again, not shown) as part of an instance of update data 2935used in extending the capabilities of the system 2000 and/or inotherwise improving its ability to suggest data preparation operationsto be performed on data sets 2130.

In various embodiments, each of the multiple node devices 2300 mayincorporate one or more of a processor 2350, a neural network 2355, astorage 2360 and a network interface 2390 to couple each of the nodedevices 2300 to the network 2999. The processor 2350 may incorporatemultiple processor cores 2351 among which operations may be distributed.The storage 2360 may store a control routine 2340. As will be explainedin greater detail, depending on the operations that the multiple nodedevices 2300 are caused to perform by the coordinating device 2500, thestorage 2360 may, at various times, additionally store one or more dataset portions 2131 of data set(s) 2130 and/or one or more training dataset portions 2111 of training data set(s) 2110 received from the one ormore storage devices 2100; and/or one or more of feature routines 2240,an operating data structure 2330, a training data structure 2310 and/orsuggestion model(s) 2470 provided by the coordinating device 2500. Thecontrol routine 2340 may incorporate a sequence of instructionsoperative on the processor(s) 2350 of each of the node devices 2300 toimplement logic to perform various functions, at least partially inparallel with the processor(s) 2350 of others of the node device 2300.In executing the control routine 2340, the processor 2350 of each of thenode devices 2300 may perform various operations under the control ofthe coordinating device 2500.

In various embodiments, the control device 2500 may incorporate one ormore of a processor 2550, a neural network 2555, a storage 2560, and/ora network interface 2590 to couple the control device 2500 to thenetwork 2999. The processor 2550 may incorporate multiple processorcores 2551 among which operations may be distributed. The storage 2360may store a control routine 2340. The storage 2560 may store a controlroutine 2540, the operating data structure 2330, the training datastructure 2310, a suggested selections data 2637 and an observedselections data 2337. As will be explained in greater detail, onoccasions in which the system 2000 receives either the initializationdata 2933 or an instance of the update data 2935, at least a portionthereof may also be stored within the storage 2560. The control routine2540 may incorporate a sequence of instructions operative on theprocessor(s) 2550 to implement logic to perform various functions. Inexecuting the control routine 2540, the processor 2550 of thecoordinating device 2500 may monitor the availability of each of thenode devices 2300, may assign sets of the node devices 2300 from amongthe multiple node devices 2300 to perform various operations, and maymonitor the performance of those operations by the node devices 2300.

In various embodiments, the viewing device 2700 may incorporate aprocessor 2750, a storage 2760, an input device 2720, a display 2780,and/or a network interface 2790 to couple the viewing device 2700 to thenetwork 2999. The storage 2760 may store one or more of a controlroutine 2740, the suggested selections data 2637 and the observedselections data 2337. The control routine 2740 may incorporate asequence of instructions operative on the processor 2750 to implementlogic to perform various functions. In executing the control routine2740, the processor 2750 may operate the input device 2720 and thedisplay 2780 to provide a user interface (UI) by which a user mayoperate the viewing device 2700 to inspect at least a portion of a dataset 2130, may control the performance of various analyses on a data set2130, and/or may be presented with visualizations and/or other resultsof analyses performed on a data set 2130. The processor 2750 may also becaused to operate such a UI to prompt the user to provide various piecesof contextual information concerning a data set 2130, the manner inwhich a data set 2130 is to be used, and/or still other contextualaspects.

Turning to FIGS. 14 and 15, in the depicted alternate embodiment of thedistributed processing system 2000, the storage 2360 within each of thenode devices 2300 is caused to store the one or more data sets 2130 andthe one or more training data sets 2110 therein in lieu of the storagedevices 2100 of the embodiment of FIGS. 13A-C and 15 doing so.Correspondingly, in executing the control routine 2340, the processor2350 within each of the node devices 2300 may be caused to perform theearlier described operations of receiving data values from otherdevices, and generating one or more data sets 2130 therefrom. Further,in executing the control routine 2340, the processor 2350 may be causedto provide the indication to the coordinating device 2500, via thenetwork 2999, of the availability of a data set 2130 for the performanceof data preparation operations thereon in lieu of the processor 2150 ofone of the storage devices 2100 of the embodiment of FIGS. 13A-C and 15doing so.

It should be noted that, despite the specific depiction in FIGS. 13A-Cand 14 of two example embodiments of the distributed processing system2000, still other alternate embodiments of the distributed processingsystem 2000 are possible that differ in still other ways. By way ofexample, in another alternate embodiment, the functionality of thecoordinating device may be incorporated into one or more of the nodedevices 2300 as process(es) supported on separate thread(s) by theprocessor 2350, by a separate processor within a coordinating subsystem,and/or within an isolated virtual machine. This may be done to entirelyobviate the need for a separate coordinating device 2500, or may be donewithin a particular node device 2300 to enable that node device 2300 totake over such functionality as a backup to the coordinating device2500.

Referring again to FIGS. 13A-C, 14 and 15, as recognizable to thoseskilled in the art, the control routines 2140, 2340, 2540 and 2740,including the components of which each is composed, are selected to beoperative on whatever type of processing component(s) that are selectedto implement applicable ones of the processors 2150, 2350, 2550 and/or2750. In various embodiments, each of these routines may include one ormore of an operating system, device drivers and/or application-levelroutines (e.g., so-called “software suites” provided on disc media,“applets” obtained from a remote server, etc.). Where an operatingsystem is included, the operating system may be any of a variety ofavailable operating systems appropriate for the processors 2150, 2350,2550 and/or 2750. Where one or more device drivers are included, thosedevice drivers may provide support for any of a variety of othercomponents, whether hardware or software components, of the storagedevices 2100, the node devices 2300, the control device 2500 and/or theviewing device 2700 (or of virtual machines employed to implement any ofthese devices in virtual form).

FIGS. 16A, 16B, 16C and 16D, together and in greater detail, illustratean example of an embodiment of performing an initial preparation of thedistributed processing system 2000 for use, including an initialtraining of a set of suggestion models 2470 thereof. FIG. 16Aillustrates aspects of the reception and distribution of theinitialization data 2933 to provide the devices 2100, 2300 and/or 2500of the system 2000 with various items required to at least beginpreparation of the system 2000 for normal operation. FIG. 16Billustrates aspects of an embodiment of the training data structure 2310employed in training the set of suggestion models 2470. FIGS. 16C-D,together, illustrate aspects of performing the training of the set ofsuggestion models 2470.

Turning more specifically to FIGS. 16A-B, the distributed processingsystem 2000 may be provided with the initialization data 2933 as part ofpreparing the distributed processing system 2000 to generate suggestionsof data preparation operations to perform on a data set 2130. Asdepicted, the initialization data 2933 may include a set of the featureroutines 2240, a set of feature vectors 2113 that form the trainingmetadata 2115, a set of context vectors 2313 that form the trainingcontext data 2315, a set of action indications 2319 that form thetraining selections data 2317, and/or a set of the suggestion models2470. As also depicted, the initialization data 2933 may also includemultiple training data sets 2110. As further depicted, while the featureroutines 2240, the feature vectors 2113, the context vectors 2313, theaction indicators 2319 and/or the suggestion models 2470 included withinthe initialization data 2933 may be provided to, and accordingly storedby, the coordinating device 2500 (or a node device 2300 performing thefunctions of the coordinating device 2500), the training data sets 2110that may also be included within the initialization data 2933 may beprovided to, and accordingly stored by, the one or more storage devices2100.

Each feature routine 2240 corresponds to a particular feature from amonga pre-selected set of features that each data set 2130 or each trainingdata set 2110 may have. In some embodiments, each feature routine 2240may include a set of instructions executable by the processor 2350within at least one of the node devices 2300 to analyze a data setportion 2131 or a training data set portion 2111 (at least partially inparallel with others of the node devices 2300) to detect thecorresponding feature. It should be noted that, in embodiments of thedistributed processing system in which the processors 2350 of differentones of the node devices 2300 are of different types that supportdiffering instruction sets, there may be more than one version of eachfeature routine 2240 that corresponds to a particular feature to enablethe detection of that particular feature within any of the node devices2300, regardless of the type of the processor 2350.

Each feature vector 2113 of the training metadata 2115 corresponds to aparticular training data set 2110 of the multiple training data sets2110 that may be stored by the one or more storage devices 2100. As willbe explained in greater detail, similar feature vectors 2133 of themetadata 2135 correspond to the data sets 2130 that may be stored by theone or more storage devices 2100. Each feature vector 2113 includes aset of feature indicators 2114 that correspond to a set of pre-selectedfeatures. In some embodiments, each of the feature indicators 2114 of afeature vector 2113 may simply indicate whether the correspondingtraining data set 2110 has the corresponding feature. In otherembodiments, one or more of the feature indicators 2114 may provide anindication of degree of the corresponding feature, or may provide anindication of a type or category associated with the feature that may beselected from a predefined set. As will be explained in greater detail,the feature indicators 2114 of each feature vector 2113 may be giventheir values by the set of feature routines 2240 as a result of the setof feature routines 2240 having been executed to analyze thecorresponding data set 2110 to detect the presence, absence and/ordegree of each feature of the set of features therein.

The features that are sought to be detected through the execution of theset of feature routines 2240, and for which indications may be includedwithin feature vectors 2113 and 2133, may include any of wide variety offeatures, including and not limited to, structural features of a dataset 2130 or training data set 2110, features of the indexing scheme bywhich data values are able to be located, and/or features of the datavalues, themselves. Thus, by way of example, the features to be sodetected may include, and are not limited to, punctuation types,delimiter types, region-specific formats, industry-specific formats, useof data containerization and/or access control, use of data compressionand/or encryption, data types of the data values, languages included,scripting and/or programming languages included, arithmetic and/orlogical operators, indexing type, index labels, current index ranges,data set size, date/time and/or indication of author and/or owner. Wheredata values include numeric values, the features to be so detected mayalso include various statistical values, including and not limited to,maximums, minimums, mean and/or median.

Each context vector 2313 of the training context data 2315 may alsocorrespond to a particular training data set of the multiple trainingdata sets 2110 that may be stored by the one or more storage devices2100. In a manner similar to the aforedescribed feature vectors 2113,similar context vectors 2333 of the context data 2335 correspond to thedata sets 2130 that may be stored by the one or more storage devices2100. Each context vector 2313 includes a set of context indicators 2314that correspond to a set of pre-selected contextual aspects. In a mannersimilar to the aforedescribed feature indicators 2114, in someembodiments, each of the context indicators 2314 of a context vector2313 may simply indicate whether the corresponding contextual aspectapplies to the corresponding training data set 2110. In otherembodiments, one or more of the context indicators 2314 may provide anindication of degree of the corresponding contextual aspect, or mayprovide an indication of a type or category associated with thecontextual aspect that may be selected from a predefined set.

It should be noted that, unlike the contextual aspects indicated incontext vectors 2333 for data sets 2130, at least some of the contextualaspects indicated in the context vectors 2313 for the set of trainingdata sets 2110 are necessarily fictitious. This arises from the factthat the training data sets 2110, unlike the data sets 2130, may existsolely for the purpose of training suggestion models 2470, and not forsuch other purposes as serving as inputs to further analyses or as basesof presentations such that any data preparation operations actually needto be performed on any of the training data sets 2110. Stateddifferently, each of the data sets 2130 contain actual data values thatwere generated in some manner, at some source, at some time andlocation, and for some purpose that gives it a context for its creation.Further, each of the data sets 2130 may have been revised one or moretimes under any of a variety of conditions each time, may have beenstored at one or more locations over time, and has been and/or issubject to various legal rights belonging to one or more persons and/orlegal entities that gives it a historical context. Still further, eachof the data sets 2130 may have been stored within the one or morestorage devices 2100 as a result of having been requested by a user of(e.g., a user of the viewing device 2700) for use as an input to afurther analysis desired by that user and/or to be presented to thatuser, thereby adding to its current context.

In contrast, a training data set 2110 generated solely to exhibit aparticular desired combination of features for purposes of efficientlytraining suggestion models 2470 will not have any such historyassociated with its generation, subsequent handling or current intendeduse such that it cannot be said to have a context that in any wayresembles that of a data set 2130. Even where, as will be explained ingreater detail, a data set 2130 is added to the set of training datasets 2110 such that it becomes a training data set 2110 that happens tohave the history of a data set 2130, the fact of its addition to the setof training data sets 2110 necessarily changes its current context. Itis for this reason that, for each training data set 2110, regardless ofits origins, at least some of the contextual aspects indicated in itscorresponding context vector 2313 are selected to provide a fictitioussimulation of a context of a data set 2130.

Each suggestion model 2470 corresponds to a different particular datapreparation operation that may be performed on a data set 2130 fromamong a set of data preparation operations. Also, each suggestion model2470 is trainable to make a determination of whether to suggest that itscorresponding data preparation operation be performed on a data set 2130based on features and contextual aspects of that data set 2130 to. Invarious embodiments, each suggestion model 2470 may be any of a varietyof types of model that is amenable for use in machine learningenvironments, including any of a variety of types of decision tree. Inembodiments of the distributed processing system 2000 in which at leasta subset of the node devices 2300 incorporate the neural network 2355,the suggestion models 2470 may be selected to be of a type that supportsimplementation using the neural network 2355. Regardless of whetherneural networks are used, in embodiments in which at least one of thesuggestion models 2470 is a decision tree, the type of decision tree maybe a contextual bandit decision tree that is selected to enable apre-selected balance to be achieved between exploitation of pastsuccesses in determining whether the performance of the correspondingdata preparation operation is to be suggested, and exploration ofoccasions on which to test making an opposite determination from the onethat would be made based on exploitation in support of further machinelearning.

Turning more specifically to FIG. 16B, as depicted, in some embodiments,the training data structure 2310 may have a structure akin to atwo-dimensional array in which pairs of one each of the feature vectors2113 and the context vectors 2313 may be organized into a set of rowsthat each correspond to one of the training data sets 2110 that may bestored within the one or more storage devices 2100. Further, the actionindications 2319 may be organized into columns that each correspond toone of the data preparation actions of the set of data preparationactions that may be performed on a data set 2130, and accordingly alsoto the corresponding suggestion model 2470. Within each such column,each action indication 2319 occupies one of the rows and serves toindicate whether the correct response of the corresponding suggestionmodel 2470 to the particular combination of feature vector 2113 andcontext vector 2313 within that row is a determination that thecorresponding data preparation operation is to be suggested, or that thecorresponding data preparation operation is NOT to be suggested. Stateddifferently, within each of the depicted columns of action indications2319, the individual action indications 2319 specify the expected outputof the corresponding suggestion model 2470 in response to the inputcombination of feature vector 2113 and context vector 2313 within thecorresponding row.

It should be noted that such a specific depiction of such atwo-dimensional array structure is merely an example of one approachthat may be taken to establish relationships between inputs and outputsof suggestion models 2470 within the training data structure 2310. Otherembodiments are possible in which the training data structure may be ofan entirely different structure in which an entirely different mechanismto associate inputs and outputs may be used. Similarly, it should benoted that other embodiments are possible in which the feature vectors2113 and the context vectors 2313 need not be vector data structures (orany other form of one-dimensional array) as has been depicted anddiscussed, and instead, could each be implemented as any of a variety ofother data structures.

Turning more specifically to FIG. 16C, as depicted, the control routine2540 of the coordinating device 2500 may include a coordinatingcomponent 2549, and/or the control routine 2340 of each of the nodedevices 2300 may include a coordinating component 2349. In embodimentsthat include the coordinating device 2500, the coordinating component2549 may be operable on the processor 2500, and each of the instances ofthe coordinating routine 2349 may be operable on their respectiveprocessors 2300, to coordinate through the network 2999 to transmit thetraining data structure 2310 from the coordinating device 2500 to eachof the node devices 2300 of the set of node devices 2300. Alternatively,in embodiments in which a node device 2300 performs the functions of thecoordinating device 2500, the coordinating component 2349 thereof may,in place of the coordinating component 2549, coordinate with itscounterparts in other node devices 2300 to transmit the training datastructure 2310 to each of those other node devices 2300 of the set ofnode devices 2300.

A similar coordination may also be employed to distribute the set ofsuggestion models 2470 among the set of node devices 2300 such that eachnode device 2300 of the set of node devices 2300 is provided with one ormore different ones of the suggestion models 2470. In FIG. 16C, anexample set of suggestion models 2470 has been designated as 2470 athrough 2470 x, and is depicted as having been distributed tocorresponding ones of an example set of node devices 2300 that has beendesignated 2300 a through 2300 x. It should be noted that this is a verysimplistic example of the distribution of a set of suggestion models2470 provided for the purpose of making clear that the distribution of aset of suggestion models 2470 does not entail transmitting the entireset to each node device 2300 of a set of node devices. It is envisionedthat, in actual implementation, there may be such a large quantity ofsuggestion models 2470 within the set of suggestion models 2470 comparedto the quantity of node devices 2300 within the set of node devices 2300that a distribution thereof may result in each such node device beingprovided with a different subset of the set of suggestion models 2470,rather than different individual suggestion model 2470 as depicted inthe simplistic example of FIG. 16C.

Referring briefly back to FIG. 16B, as well as to FIG. 16C, it should benoted that, despite the depiction and discussion herein of the entiretraining data structure 2310 being transmitted to each node device 2300of the set of node devices 2300, other embodiments are possible in whichdiffering portions of the training data structure 2310 may betransmitted to each node device 2300. More specifically, it may be thateach node device 2300 of the set of node devices 2300 is provided with aportion of the training data structure 2310 that, instead of includingall of the action indications 2319 within all of the columnscorresponding to the entire set of suggestion models 2470, includes asubset of those columns that correspond to the one or more suggestionmodels 2470 that have been distributed to that node device 2300.

Turning more specifically to FIG. 16D, as depicted, within each nodedevice 2300 of the set of node devices 2300, the control routine 2340may include a training component 2347 operable on the processor 2300 toemploy the training data structure 2310 (or the particular subset of thetraining data structure 2310 received by the node device 2300) to traineach of the one or more suggestion model 2470 received by the nodedevice 2300. More precisely, as previously discussed, each suggestionmodel 2470 is trained to make a determination of whether to suggest theperformance of a corresponding data preparation operation on aparticular data set 2130 based on the features and contextual aspects ofthat particular data set 2130. As previously discussed, each of thesuggestion models 2470 may be any of a variety of types of model, and sothe exact manner in which each suggestion model 2470 may be trained mayvary accordingly.

As also previously discussed, in embodiments in which at least a subsetof the node devices 2300 of the node device grid 2003 incorporate theneural network 2355 for use in implementing at least a subset of thesuggestion models 2470, such training may be performed through abackpropagation or other appropriate technique for the training ofneural networks. In such embodiments, such suggestion models 2470 mayinclude configuration data that specifies one or more of 1) the overallquantity and organization of neurons into layer(s); 2) the mapping ofindications of particular features and/or indications of particularcontextual aspects to neuron inputs of an input layer; 3) theconnections among neurons within and/or between layers; 4) the mappingof the indication(s) of the determination of whether to suggest a datapreparation operation is mapped to neuron output(s) of an output layer;5) aspects of the triggering function by which each neuron is triggeredby its input(s) to provide particular output(s); and 6) the weightsand/or biases used for inputs, outputs and/or the triggering functionsof the neurons. It may be that parts of such configuration data definethe quantity of neurons used, their organization into layers, themappings of inputs and/or outputs, the connections thereamong, thetriggering functions, and/or an initial set of weights and/or biases mayserve to define the type of model for a suggestion model 2470, while theprocess of being trained and/or subsequently re-trained may serve toadjust at least a subset of the weights and/or biases.

Referring briefly back to FIG. 16C, as well as to FIG. 16D, thecoordinating device 2500 (or a node device 2300 performing the functionsof the coordinating device 2500) may coordinate the training of thesuggestion models 2470 by each of the node devices 2300 of the set ofnode devices 2300 to occur at least partially in parallel. Moreprecisely, the coordinating component 2549 of the coordinating device2500 and each of the instances of the coordinating component 2349 ofeach of the node devices 2300 may enable the processor 2550 to controland/or monitor the training of each of the suggestion models 2470. Aseach suggestion model 2470 is trained, the processor 2550 may, throughthe coordinating components 2549 and 2349, coordinate the transmissionof each of the now trained suggestion models 2470 back to thecoordinating device 2500 to be stored for use with data sets 2130, aswill shortly be described in greater detail. Alternatively oradditionally, as each suggestion model 2470 is trained, each of the nodedevices 2300 of the set of node devices 2300 may locally store the oneor more suggestion models 2470 provided to it for training to obviatethe need to again distribute the set of suggestion models 2470 as partof the set of node devices 2300 subsequently using the set of suggestionmodels 2470.

FIGS. 17A, 17B, 17C, 17D, 17E and 17F, together and in greater detail,illustrate an example of an embodiment of performing feature detection,determining what data preparation operation to suggest be performed andselectively updating the training data structure 2310 for subsequentre-training of suggestion models 2470. FIG. 17A illustrates aspects ofthe provision, exchange and use of various pieces of data in performingthese operations. FIG. 17B illustrates aspects of an embodiment ofreceiving at least some contextual aspects of a data set 2130. FIG. 17Cillustrates aspects of an embodiment of performing feature detection todetect features of the data set 2130. FIGS. 17D-E, together, illustrateaspects of an embodiment of determining which data preparationoperations to suggest be performed on the data set 2130. FIG. 17Fillustrates aspects of selectively augmenting the training datastructure 2310 with indications of what data preparation operations wereactually selected to be performed correlated to indications of thedetected features and various contextual aspects in preparation forsubsequent re-training.

Turning to FIGS. 17A-B, as depicted, the control routine 2540 of thecoordinating device 2500 may include a context component 2543 operativeon the processor 2500 (or the control routine 2340 of a node deviceperforming the functions of the coordinating device 2500 may include acontext component 2343 operative on the processor 2300 thereof) toeither generate a context vector 2333 of the context data 2335 withinthe operating data structure 2330, or select a context vector 2333 forinclusion in the context data 2335 within the operating data structure2330, based on received pieces of information about the context of adata set 2130. This may be among the operations that are triggered bythe coordinating device 2500 (or a node device 2300 performing thefunctions of the coordinating device 2500) being made aware of theavailability of the data set 2130 in any of a variety of ways. In someembodiments, the one or more storage devices 2100 may transmit anindication of a data set 2130 having become available to thecoordinating device 2500. Alternatively or additionally, thecoordinating device 2500 may be made aware of the availability of a dataset 2130 as a result of receiving a request for access thereto from theviewing device 2700.

It may be that the distributed processing system 2000 is operated by anyof a wide variety of commercial, academic and/or governmental entitiesto perform data preparation operations and/or still other operations onnumerous relatively large data sets 2130, and the one or more storagedevices 2100 may be employed to store a built-up queue of data set 2130that are to each be put through various data preparation operationsbefore then being put through any of a variety of data analysisoperations and/or being used as a basis for the generation of any of avariety of presentations of information. As previously discussed, insome embodiments, the one or more storage devices 2100 may receive adata set 2130 in completed form from a source (not shown) that isexternal to the system 2000, thereby triggering the transmission of theindication of availability of the data set 2130 to the coordinatingdevice 2500. In other embodiments, the one or more storage devices 2100may receive portions of a data set 2100 over time from one or moreexternal devices (e.g., an array of sensor devices, etc.), and mayassemble the data set 2100 from those received portions until the lastportions are so received and the data set is complete, which may thentrigger the transmission of the indication of availability of the dataset 2130 to the coordinating device 2500.

Regardless of the exact manner in which the one or more storage devices2100 receive a data set 2130, the process of receiving the data set 2130may enable various pieces contextual information to be received with it.By way of example, where the data set 2130 is received in completed formfrom an external source, the data set 2130 may be conveyed to the one ormore storage devices 2100 as a data file or any of a variety of othertypes of data container structure that includes a file header or otherdescriptive data structure. Such a header or other descriptive datastructure may include indications of the identity of the source of thedata set 2130 and/or the data values within it; aspects of the when,why, how and/or where of the generation of the data set 2130; whatindustry standard(s) and/or version levels thereof for formatting,compression and/or encryption may be applicable to the data set 2130;etc. By way of another example, where the data set 2130 is received inportions over time and for assembly by the one or more storage devices2100, the one or more storage devices 2100 may already be provided withvarious pieces of contextual information concerning the source(s) ofthose portions as part of facilitating the establishment ofcommunications to support the receipt of those portions. Sucharrangements may entail one or more agreements and/or the establishmentof one or more accounts with various pieces of account information; oneor more pre-defined network addresses that may be polled by the one ormore storage devices 2100 on a recurring basis; descriptive informationof the one or more external devices from which the portions are receivedover time; etc.

It may be that the distributed processing system 2000 is operated by anyof a wide variety of commercial, academic and/or governmental entitiesas a distributed library system by which various individuals of thatentity may operate viewing devices, such as the viewing device 2700, torequest access to any of a wide variety of data sets 2130 that may bemaintained by any of a wide variety of other entities serving as sourceswith which various licensing and/or other content access arrangementsmay have been made. Thus, a user of the viewing device 2700 may employ auser interface 2870 provided thereby to enter a request that is relayedto the coordinating device 2500 to access a particular data set 2130. Inresponse to receiving the request, the coordinating device 2500 may, inturn, relay the request to one or more devices external to the system2000 that are associated with such licensing and/or other content accessarrangements. Upon receiving the requested data set 2130, thecoordinating device 2500 may directly store it within the one or morestorage devices 2100 or may in arrange for it to be provided to the oneor more storage devices 2100.

Regardless of the exact manner in which the one or more storage devices2100 are provided with the requested data set 2130, various piecescontextual information may be received by the coordinating device fromboth the viewing device 2700 and the external device(s) that may providethe data set 2130. By way of example, as part of making the request toaccess the data set 2130, the user of the viewing device 2700 mayprovide any of a variety of differing pieces of contextual informationabout the data set 2130 that may be used to search for and identify it.Such information may include indications of the identity of the sourceof the data set 2130 and/or the data values within it; aspects of thewhen, why, how and/or where of the generation of the data set 2130; etc.Additionally, as part of receiving the data set 2130 from the externalsource(s), the coordinating device may also receive further pieces ofcontextual information about it. Again, this may arise from the data set2130 being received as a data file or any of a variety of other types ofdata container structure that includes a file header or otherdescriptive data structure.

As depicted, and similar to what has been depicted and described for thetraining context data 2315, each of the context vectors 2333 of thecontext data 2335 may take the form of a vector data structure (e.g., aone-dimensional array) with a set of storage locations allocated for aset of context indicators 2334. Indeed, in some embodiments, the contextvectors 2333 of the training context data 2335 may be of identical sizeand configuration to the context vectors 2313 of the training contextdata 2315, and may have the same number and arrangement of contextindicators 2334 and 2314, respectively, that are indicative of the samecontextual aspects. Similar to the context indicators 2314 of thecontext vectors 2313, in some embodiments, the context indicators 2334of the context vectors 2333 may simply indicate whether thecorresponding contextual aspect applies to the data set 2130. In otherembodiments, one or more of the context indicators 2334 may provide anindication of degree of the corresponding contextual aspect, or mayprovide an indication of a type or category associated with thecontextual aspect.

In some embodiments, the context component 2543 may generate the contextvector 2333 that corresponds to the data set 2130 from the pieces ofcontext information received from whichever one(s) of the viewing device2700, from the external source device and/or from the one or morestorage devices 2100 have provided contextual information. In otherembodiments, the context component 2543 may use one or more of suchreceived pieces of context information to select the context vector 2333that corresponds to the data set 2130 from among multiple availablecontext vectors 2333 (not shown). By way of example, it may be that theuser of the viewing device 2700 is required to login to an accountassigned to that use on the viewing device 2700, and that account mayassociate that user with such user-related contextual aspects as acountry or region, industry or field of study, language and/or type ofcommunications capability (e.g., whether the user is able to hear or isdeaf), access privileges regarding data sets 2130 and/or specificvarieties of data values therein, etc. Thus, the receipt of anindication of the identity of the user may trigger the selection of acontext vector 2333 that specifies such contextual aspects of the userthat then also become contextual aspects of the data set 2130 that theuser may request access to.

The contextual aspects of the data set 2130 that are included in thecontext vector 2333 may include any of a variety of aspects, includingand not limited to, aspects of when and how the data set 2130 wasgenerated, aspects of the source of the data set 2130 and/or the datatherein, aspects of legal and/or other rights associated with the dataset 2130 and/or the data therein, etc. Thus, by way of example, thecontextual aspects may include, and are not limited to, the when, where,how, why and/or by who the data set 2130 and/or the data 2130 thereinwas generated; where the data set 2130 is and/or has been stored;history of revisions to the data set 2130; owners, creators, licensees,licensors, custodians, etc. of the data set 2130; and/or copyrights,licensing terms, publication conditions, access restrictions, etc. ofthe data.

Regardless of the exact manner in which the context vector 2333associated with the data set 2130 may be generated and/or selected, thecoordinating device may additionally use such received contextualinformation to assign a higher or lower priority to the data set 2130versus other data sets 2130 such that the order in which the performanceof data preparation operations may be performed on data sets 2130 in aqueue may be changed. By way of example, the identity of the source ofthe data set 2130 and/or of the user may cause the data set 2130 to beassigned a high enough priority as to become the next data set 2130 onwhich data preparation operations are to be performed despite a lengthyqueue of other data sets 2130 having been available for longer periodsof time. Alternatively or additionally, the coordinating device may usesuch information concerning the feature of size of the data set 2130,along with recurringly received indications of which node devices 2300of the distributed processing system 2000 are available to determine howmany node devices 2300 of the distributed processing system 2000, aswell as which ones, to include in the set of node devices 2300.

Turning to FIG. 17C, the generation of a feature vector 2134 of themetadata 2135 within the operating data structure 2330 for the data set2130 may be triggered along with the aforedescribed generation and/orselection of the corresponding context vector 2334. Through thecoordinating components 2349 and 2549 and the network 2999, thecoordinating device 2500 may communicate with each of the node devices2300 of the set of node devices 2300 to divide the data set 2130 intodata set portions 2131 and to distribute those data set portions 2131among the set of node devices 2300. In some embodiments, the data set2130 may be divided into data set portions 2131 of equal (or nearlyequal) size as part of distributing the processing and storagerequirements of the data set 2130 among the set of node devices 2300relatively equally. The coordinating device 2500 may transmit, to eachnode device 2300 of the set of node devices 2300, a pointer or otherindication as to the storage location(s) within one or more storagedevices 2100 at which the data set portion 2131 assigned to it may beretrieved. In other embodiments, the coordinating device 2500 may,itself, retrieve each data set portion 2131 from one or more storagedevices 2100 and relay each to the node device 2300 to data set portion2131 has been assigned.

With the data set portions 2131 of the data set 2130 distributed amongthe set of node devices 2300, the coordinating device 2500 may transmita set of feature routines 2240 to each node device 2300 of the set ofnode devices 2300. Each feature routine 2240 corresponds to a particularfeature that the data set 2130 may have, and each feature routine 2240may include a set of instructions executable by the processor 2350within a node device 2300 to analyze a corresponding one of the data setportions 2131 to detect the corresponding feature. Each of the nodedevices 2300 of the set of node devices 2300 may execute each of thefeature routines 2240 of the set of feature routines 2240 to determinewhether any of the features detectable through the execution thereof arepresent within the data set portion assigned to that node device 2300.The coordinating device 2500 may coordinate such execution of the set offeature routines 2240 by each node device 2300 of the set of nodedevices 2300 to occur at least partially in parallel. As such executionof the feature routines 2240 by the set of node devices occurs, each ofthe node devices 2300 of the set of node devices 2300 may transmitindications of detected features to the coordinating device 2500 via thenetwork 2999.

The features sought to be detected through the execution of the set offeature routines 2240 may include any of wide variety of features,including and not limited to, structural features of the data set 2130,features of the indexing scheme by which data values of the data set2130 are able to be located, and/or features of the data values,themselves. Thus, by way of example, the features to be so detected mayinclude, and are not limited to, punctuation types, delimiter types,region-specific formats, industry-specific formats, use of datacontainerization and/or access control, use of data compression and/orencryption, data types of the data values, languages included, scriptingand/or programming languages included, arithmetic and/or logicaloperators, indexing type, index labels, current index ranges, data setsize, date/time and/or indication of author and/or owner. Where datavalues of the data set 2130 include numeric values, the features to beso detected may also include various statistical values, including andnot limited to, maximums, minimums, mean and/or median.

As previously discussed, in some embodiments, the coordinating device2500 may cooperate with the set of node devices 2300 through the network2999 to exchange at least a subset of the indications of detectedfeatures among the node devices 2300 within the set of node devices2300, and may do so in a manner similar to what is disclosed in thepreviously mentioned U.S. Pat. No. 9,753,767 issued Sep. 5, 2017. Again,as discussed therein, the detection of one or more features of the dataset 2130 may be assisted by, guided by and/or triggered by whether oneor more other features of the data set 2130 have been detected.

Regardless of the exact manner in which feature detection is performed,the coordinating device 2500 may generate the feature vector 2134 forthe data set 2130 based on the indications of detected features receivedfrom the set of node devices 2300. As depicted, and similar to what hasbeen depicted and described for the training metadata 2115, each of thefeature vectors 2133 of the metadata 2135 may take the form of a vectordata structure (e.g., a one-dimensional array) with a set of storagelocations allocated for a set of context indicators 2134. Indeed, insome embodiments, the feature vectors 2133 of the metadata 2135 may beof identical size and configuration to the feature vectors 2113 of thetraining metadata 2115, and may have the same number and arrangement offeature indicators 2134 and 2114, respectively, that are indicative ofthe same features. Similar to the feature indicators 2114 of the featurevectors 2113, in some embodiments, the feature indicators 2134 of thefeature vectors 2133 may simply indicate whether the correspondingfeatures are found to be present in the data set 2130. In otherembodiments, one or more of the feature indicators 2134 may provide anindication of degree of the corresponding feature, or may provide anindication of a type or category associated with the feature.

Turning to FIGS. 17D-E, with the feature vector 2133 and the contextvector 2333 corresponding to the data set 2130 having been generated,the coordinating device 2500 may transmit both (e.g., within theoperating data structure 2330, as depicted) to each node device 2300 ofthe set of node devices 2300. The coordinating device 2500 may alsodistribute a set of suggestion models 2470 among the set of node devices2300, with each node device 2300 receiving one or more differentsuggestion models 2470 from the other node devices 2300. Each suggestionmodel 2470 corresponds to a different particular data preparationoperation that may be performed on the data set 2130 from among a set ofdata preparation operations. Each suggestion model 2470 may be any of avariety of type of machine learning model, and each may have beenpreviously trained to determine whether to suggest that itscorresponding data preparation operation be performed on a data set 2130based on detected features and contextual aspects thereof. In someembodiments, at least one of the suggestion models may be a contextualbandit decision tree selected to achieve a pre-selected balance betweenexploitation of past successes in determining whether the performance ofthe corresponding data preparation operation is to be suggested, andexploration of occasions on which to test making an oppositedetermination from the one that would be made based on exploitation insupport of further machine learning.

With the feature vector 2133 and the context vector 2333 transmitted toeach of the node devices 2300 of the set of node devices 2300, and withthe set of suggestion models 2470 distributed among the node devices2300, each of the node devices 2300 may employ the feature vector 2133and the context vector 2333 as inputs to each of the one or moresuggestion models 2470 distributed to it to derive a separatedetermination from each suggestion model 2470 of whether itscorresponding data preparation operation is to be suggested to beperformed on the data set 2130. The coordinating device 2500 maycoordinate such uses of the set of suggestion models 2470 by the set ofnode devices 2300 to occur at least partially in parallel. As suchdeterminations are made, each of the node devices 2300 of the set ofnode devices 2300 may provide indications of such determinations to thecoordinating device 2470.

The data preparation operations may include any of a variety of types ofoperations, including and not limited to: data value and/or formatnormalizations; data transformations; data filtering, stripping and/ormasking; and/or data various data analyses in support of the generationof various graphical presentations. Such operations may serve to changedata values, the selection of data values, the format of data values,the arrangement of data values within a data set, the structure of adata set, the indexing scheme of a data set, etc. Alternatively oradditionally, such operations may serve to remove data values forreasons of data security and/or to comply with data privacy (e.g.,legally mandated personal medical data privacy restrictions),intellectual property protections (e.g., copyright), licensing terms,etc. Any of such operations may be performed to cause a data set and/orthe data values thereof to fit what is needed for different geographicregions, different legal jurisdictions, different languages, differentindustries, different scientific fields, different entities (e.g.,convert among corporate, academic and/or governmental entities), etc.

Based on the indications received by the coordinating device 2500 ofwhich data preparation operations are to be suggested to be performed onthe data set 2130, the coordinating device 2470 may generate thesuggested selections data 2637 to indicate the subset of datapreparation operations that are to be suggested to be so performed. Thecoordinating device 2500 may then transmit the suggested selections data2637 to the viewing device 2700 to enable the presentation of thesuggested subset to the user thereof via the UI 2870 thereof. In someembodiments, the UI 2870 may be operable to enable the user of theviewing device 2700 to view (or otherwise inspect) portions and/orvarious aspects of the data set 2130 manually to determine whether theuser agrees with the suggested subset indicated by the suggestedselections data 2637. Via the UI 2870, the user may provide inputindicating that the suggested subset is selected to be the subset ofdata preparation operations that are to be performed on the data set, orthat a different subset of the set of data preparation operations isbeing manually selected to be so performed.

Turning to FIG. 17F, as depicted, the viewing device 2700 may generateand transmit to the coordinating device 2500 (or to a node device 2300performing the functions of the coordinating device 2500) the observedselections data 2337 to indicate the subset of data preparationoperations that have actually been selected by the user of the viewingdevice 2700 to be performed on the data set 2130. Upon receipt of theobserved selections data 2337, the coordinating device may coordinatethe performance of the selected subset of data preparation operations onthe data set 2130 with the set of node devices 2300. In someembodiments, each node device 2300 of the set of node devices 2300 mayhave continued to store the data set portion 2131 of the data set 2130that was distributed to it as part of the aforedescribed detection offeatures. In such embodiments, and depending on such factors as theamount of time that has elapsed since the aforedescribed detection offeatures, advantage may be taken of such distribution of the data set2130 among the set of node devices 2300 by causing the set of nodedevices 2300 to then perform the selected subset of data preparationoperations on the data set portions 2131 in situ.

Also upon receipt of the observed selections data 2337 from the viewingdevice 2700, the coordinating device 2500 may compare the selectedsubset to the suggested subset to determine whether there are anydifferences therebetween. If there are no differences, then thesuggested subset may be deemed to represent a set of successfuldeterminations by the full set of suggestion models 2470 of which datapreparation operations are to be suggested to the user of the viewingdevice 2700. In some embodiments, the coordinating device 2500 maymaintain a count, a score or other indication for each suggestion model2470 that reflects the rate of the ability of each suggestion model 2470to successfully make such determinations. Such an indication of successrate may be updated to reflect each instance of a success and/or lackthereof in making such a determination for each suggestion model 2470,and such an indication may be employed as an input to any subsequentre-training of the set of suggestion models 2470.

However, if there are differences between the suggested subset and theselected subset, then the coordinating device 2500 may add the featurevector 2133, the context vector 2333 and an indication of the selectedsubset to the training data structure 2310. In some embodiments, such anaddition to the training data structure 2310 may always occur wherethere are differences between the suggested subset and the selectedsubset. In other embodiments, whether such addition occurs may be atleast partially determined by a filtering or other limiting algorithmthat may be part of an overall machine learning algorithm. As will befamiliar to those skilled in the art, while the use of decision treesand/or similar models as the suggestion models may be deemed relativelyeffective in making such determinations, decision trees are subject toall too easily learning wrong lessons from occasional bad input. Moreprecisely, there may be instances in which the user of the viewingdevice 2700 makes one or more errant selections of data preparationoperations to be performed or to not be performed. This may arise in thecase of an inexperienced user or where the data set 2130 is of a typethat the user isn't as experienced in working with. Where one or more ofthe suggestion models 2470 are implemented as a type of decision tree,those errantly selected or errantly non-selected data preparationoperations may be all too easily learned, thereby resulting in futureincorrect determinations from one or more of the suggestion models 2470of whether to suggest the performance of those data preparationoperations.

To counter this, in some embodiments, any of a variety of samplingalgorithms may be used to control whether the training data structure2310 is to be augmented with the feature vector 2133, the context vector2333 and an indication of the selected subset in response to theselected subset differing from the suggested subset. Such use ofsampling to limit occasions on which such additions are made may bebased on a presumption that, even though there may be occasionalmistakes made by a user in specifying a subset of the data preparationoperations to be performed on a data set, the user is more likelyspecify a correct subset on the majority of occasions. Thus, byeffectively slowing the rate at which such additions are made to thetraining data structure 2310, in essence, such use of sampling serves toreduce the likelihood of incorporating such occasional mistakes intofuture re-training.

FIGS. 18A and 18B, together and in greater detail, illustrate an exampleof an embodiment of performing an update of one or more items of thedistributed processing system 2000 to improve its functionality. FIG.18A illustrates aspects of the reception and distribution of an instanceof the update data 2935 to provide the devices 2100, 2300 and/or 2500 ofthe system 2000 with various new items to extend the functionality of,and/or to improve the accuracy of, the system 2000 in normal operation.FIG. 18B illustrates aspects of an embodiment of performing featuredetection to detect features of a training data set 2110 as may betriggered by the receipt of an instance of the update data 2935.

Turning more specifically to FIG. 18A, the distributed processing system2000 may be provided with an instance of the update data 2935 as part ofongoing efforts by an operator of the system 2000 to extend itscapabilities and/or to improve the accuracy with which it performsvarious operations. As depicted, an instance of the update data 2935 mayinclude one or more feature routines 2240, one or more feature vectors2113, one or more context vectors 2313, one or more action indications2319, and/or one or more suggestion models 2470. As also depicted, aninstance of the update data 2935 may also include one or more trainingdata sets 2110. Similar to the initialization data 2933, while thefeature routines 2240, the feature vector(s) 2113, the context vector(s)2313, the action indicator(s) 2319 and/or the suggestion model(s) 2470included within an instance of the update data 2935 may be provided to,and accordingly stored by, the coordinating device 2500 (or a nodedevice 2300 performing the functions of the coordinating device 2500),the training data set(s) 2110 that may also be included within aninstance of the update data 2935 may be provided to, and accordinglystored by, the one or more storage devices 2100. Unlike theinitialization data 2933, which contains full sets of most, if not all,of these items such that the system 2000 is able to be provided withwhat is needed from the initialization data 2933 to begin to be put tonormal use, instances of the update data 2935 may contain relativelysmall subsets of new ones of these items for the purpose of augmentingthe existing sets of these items with new items that extendfunctionality, and/or replacing portions of those sets with new itemsthat improve functionality.

Depending on what new items are included in an instance of the updatedata 2935, and depending on whether complete sets of new items meant toreplace existing sets of items are included, the coordinating device2500 (or a node device 2300 performing the functions of the coordinatingdevice 2500) may respond to the receipt of an instance of the updatedata 2935 in a variety of ways. By way of example, if an instance of theupdate data 2935 includes a full set of new suggestion models 2740accompanied by full sets of new feature vectors 2113, new contextvectors 2313 and new action indications 2319 (e.g., completely newversions of the training metadata 2115, the training context data 2315and the training selections data 2317, respectively) for use in trainingthe full set of new suggestion models 2740, then the processor 2550 ofthe coordinating device 2500 may be caused to respond to the receipt ofsuch an instance of the update data 2935 by replacing the existing fullsets of the suggestion models 2740, feature vectors 2113, contextvectors 2313 and action indications 2319 already stored within thestorage 2560 with the new full sets, followed by performing the trainingof the new set of suggestion models 2740. However, if less than a fullset of new suggestion models 2740 is included in an instance of theupdate data 2935, and is accompanied by corresponding new featurevectors 2113, context vectors 2313 and action indications 2319, then theprocessor 2950 of the coordinating device 2900 may respond by replacingjust a subset of the existing suggestion models 2740 with the newsuggestion models 2740 if the new suggestion models 2740 are meant to bereplacements, or by augmenting the existing set of suggestion models2740 to additionally include the new suggestion models 2740 if the newsuggestion models 2740 are meant to be additions. Correspondingly, thenew feature vectors 2113 may replace a subset of or be added to theexisting set of feature vectors 2113 (i.e., the training metadata 2115),the new context vectors 2313 may replace a subset of or be added to theexisting set of context vectors 2313 (i.e., the training context data2315), and the new action indications 2319 may replace a subset of or beadded to the existing set of action indications 2319 (i.e., the trainingselections data 2317). Following such replacements or additions, the newfeature vectors 2113, the new context vectors 2313 and the new actionindications 2319 may then be used to train the new suggestion models2740.

The replacement of some or all of the existing suggestion models 2740 inthe set of suggestion models 2740 that are stored by the coordinatingdevice 2500 may be deemed desirable in situations where it may bedetermined that a different type of model (e.g., a different type ofdecision tree) has been determined to be a better choice than the typeof model (e.g., the type of decision tree) that may have been selectedfor the existing suggestion models 2740. As an alternative, it shouldalso be noted that an instance of the update data 2935 may not include areplacement for an existing suggestion model 2740, but may include oneor more new feature vectors 2113, one or more new context vectors 2313and/or one or more new action indications 2319 that may replace existingones that had been previously used to train that existing suggestionmodel 2740. This may be deemed desirable where one or more of theseitems for training that existing suggestion model 2740 have been foundto contain errors and/or are otherwise deemed to be in need ofimprovement to improve the training of that existing suggestion model2740.

In contrast, the addition of one or more new suggestion models 2740 toan existing set of suggestion models 2740 that are stored by thecoordinating device 2500 may be part of adding support for new datapreparation operations by adding the corresponding ability to makedeterminations of whether the performance of those new data preparationoperations are to be suggested to a user. Such a new suggestion model2740 that is provided to augment the existing set of suggestion models2740 may be accompanied by corresponding new feature vectors 2113, newcontext vectors 2313 and new action indications 2319 that are needed totrain the new suggestion model 2740, and which are added tocorresponding ones of the existing set of feature vectors 2113 withinthe training metadata 2115, the existing set of context vectors 2313within the training context data 2315, and the existing set of actionindications 2319 within the training selections data 2317.

However, in some embodiments, there may also be instances of the updatedata 2935 that may contain complete new sets of these items such thatthey resemble the initialization data 2933 as their contents mayentirely replace all of such existing items that may have beenpreviously provided to a system 2000 to an extent that they effectivelyserve as the basis for a repeat of the initialization of the system2000. On occasions in which such a complete replacement occurs, theresult may effectively be a wiping away of all machine learning that hadbeen accomplished based on the existing sets of items. Thus, in someembodiments in which the performance of such a complete replacement issupported, the coordinating device 2500 may be triggered to store a copyof the existing sets of these items within the one or more storagedevices 2100 and/or to transmit a copy of the existing sets of theseitems to another device external to the system 2000 to enablepreservation of the machine learning that has taken place for analysis.

Turning more specifically to FIG. 18B, where an instance of the updatedata 2935 includes one or more new feature routines 2240 and/or includesone or more new training data sets 2110, then the aforedescribedre-training of one or more suggestion models 2470 may need to bepreceded with the use of the set of feature routines 2240 to generateone or more new feature vectors 2113 of the training metadata 2115. Thismay arise from the fact that the set of feature vectors 2113 of thetraining metadata 2115 are used as inputs to the training of the set ofsuggestion models 2470.

As has been discussed, each feature vector 2113 of the training metadata2115 may be generated by the execution of the set of feature routines2240 to analyze a corresponding training data set 2110 to determinewhich features it may have of the corresponding set of features.Therefore, if either the training data set 2110 or one of the featureroutines 2240 that were used to generate a particular feature vector2113 is changed, then the particular feature vector 2113 may need to bere-generated to correctly indicate which features of the set of featuresare present within the current version of the training data set 2110, asdetermined by the current set of feature routines 2240.

However, it should be noted that, in some embodiments, one or more newfeature routines 2240 may be included in an instance of the update datato augment the existing set of feature routines 2240, rather than toreplace existing feature routines 2240. This may be done to add theability to detect entirely new features as another way to extend thecapabilities of the distributed processing system 2000. As previouslydiscussed, each feature vector 2113 of the training metadata 2115includes a set of feature indicators 2114 that each correspond to one ofthe features of the set of features that may be detected in acorresponding training data set 2110, and correspondingly, each featurevector 2133 of the metadata 2135 includes a set of feature indicators2134 that each correspond to one of the features of the same set offeatures that may be detected in a corresponding data set 2130. Thus,the addition of a new feature routine 2240 to detect a new feature maynecessitate adding to the set of feature indicators 2114 and 2134 withineach feature vector 2113 and 2133, respectively, to support the additionof the new feature to the set of features that are able to be detected.As a result, each training data set 2110 may need to be put back througha performance of feature detection by the system 2000 to re-generate anew set of feature vectors 2113 within the training metadata 2115 thateach include the addition new feature indicator 2114 for the newfeature. This, in turn, may necessitate a re-training of the set ofsuggestion models 2470 with the set of new feature vectors 2113.

FIGS. 19A, 19B, 19C and 19D, together, illustrate an example embodimentof a logic flow 3100. The logic flow 3100 may be representative of someor all of the operations executed by one or more embodiments describedherein. More specifically, the logic flow 3100 may illustrate operationsperformed by the processor 2550 in executing the control routine 2540,and/or performed by other component(s) of the coordinating device 2500.Alternatively, the logic flow 3100 may illustrate operations performedby the processor 2350 in executing the control routine 2340, and/orperformed by other component(s) of a node device 2300 that is performingthe functions of the coordinating device 2500.

At 3110, a processor of a coordinating device of a distributedprocessing system (e.g., the processor 2550 of the coordinating device2500 of the distributed processing system 2000) may receive, via anetwork, either initialization data or an instance of update data (e.g.,via the network 2999, and either the initialization data 2933 or aninstance of the update data 2935). As has been discussed, theinitialization data may be received as part of preparing the system fornormal use, while instances of the update data may be received by thedistributed processing system over time during such normal use as partof a mechanism to extend and/or improve the capabilities of thedistributed processing system.

At 3120, the processor may check whether the received data isinitialization data that is provided to prepare the system for normaluse. As previously discussed, such received initialization data mayinclude sets of various items needed to enable the system to detectfeatures in data sets (e.g., features within data sets 2130 and/ortraining data sets 2110), and to suggest a subset of a set of datapreparation operations to perform on a data set based on its featuresand context. If the received data is initialization data, then theprocessor may store the various received sets of items in theinitialization data as part of preparing the system for use. Morespecifically, at 3122, the processor may store the received sets offeature routines and suggestion models (e.g., sets of the featureroutines 2240 and the suggestion models 2470). At 3123, the processormay store the received set of feature vectors as training metadatawithin a training data structure, may store the received set of contextvectors as training context data within the training data structure, andmay store the received set of action indications as training selectionsdata within the training data structure (e.g., the training metadata2115, the training context data 2315, and the training selections data2317, respectively, within the training data structure 2310). At 3125,the processor may transmit the training data structure to each nodedevice of a set of node devices selected for use in training the set ofsuggestion models. At 3126, the processor may distribute the set ofsuggestion models among the set of node devices to enable the set ofsuggestion models to be trained by the set of node devices, at leastpartially in parallel, and using the training data structure. At 3128,the processor may retrieve and store the now trained set of suggestionmodels from the set of node devices in preparation for use.

However, if at 3120, the received data is not initialization data, thenat 3130, the processor may check whether the received data is aninstance of update data containing full sets of replacements for all of:the existing set of feature routines, the existing set of suggestionmodels, the existing set of feature vectors, the existing set of contextvectors and the existing set of action indications. If so, then theprocessor may store the various received sets of items in the instanceof update data as full replacements for the existing sets of items. Morespecifically, at 3132, the processor may store the received sets offeature routines and suggestion models as replacements for the existingsets thereof. At 3133, the processor may store the received set offeature vectors, context vectors and action indications as replacementsfor existing sets thereof, before proceeding to transmit training datastructure to each node device of a set of node devices at 3125.

However, if at 3130, the received data is not an instance of update datacontaining full sets of replacements for all of such sets of items, thena presumption is made that the received data is an instance of updatedata that contains new additional items and/or new replacement items forless than all items in all existing sets. More specifically, at 3140,the processor may check whether the received data is an instance ofupdate data that includes new feature vectors, context vectors, actionindications and/or suggestion models that are provided as replacementsfor existing ones within the existing sets and/or are provided asadditions to the existing sets. If not at 3140, then at 3150, theprocessor may store any new replacement feature routines in the receiveddata as replacement for whichever feature routines that they are meantto replace within the existing set of feature routines, and may storeany new additional feature routines as additions to the existing set offeature routines at 3151. At 3153, the processor may at least coordinate(if not more directly effect) the distribution of training data setportions (e.g., the training data set portions 2111) of one or moretraining data sets among a set of node devices selected to performfeature detection, and may transmit the existing set of feature routinesto each node device of the set of node devices at 3154 to enable each toperform feature detection on training data set portions of the one ormore training data sets at least partially in parallel.

As the set of node devices perform feature detection, at 3156, theprocessor may receive indications from the set of node devices offeatures that have been detected in each of the one or more trainingdata sets. As has been discussed, the processor may engage in, or atleast coordinate, exchanges of indications among the set of node devicesof features that have been detected to support instances in which thedetection of one feature has a dependency on the detection of anotherfeature by what may be a different a node device. At 3158, the processormay generate new feature vectors based on the received indications ofdetected features, and may store the new feature vectors as replacementsfor the existing set of feature vectors within the training datastructure. At 3160, the processor may transmit the training datastructure with the newly generated set of feature vectors to each of thenode devices of the set of node devices. At 3162, the processor maydistribute the set of suggestion models among the set of node devices toenable a re-training of the set of suggestion models by the set of nodedevices based on the training data structure within the new set offeature vectors. At 3164, the processor may retrieve the set of newlyre-trained suggestion models from the set of node devices.

However, if at 3140, the received data is an instance of update datathat includes new feature vectors, then at 3142, the processor may storeany new replacement suggestion models as replacements for whicheverspecific suggestion models that they are meant to replace within theexisting set of suggestion models. At 3143, the processor may store anynew replacement feature vectors, context vectors and/or actionindications as replacements for whichever specific feature vectors,context vectors and/or action indications that they are meant to replacewithin corresponding ones of their respective existing sets. At 3145,the processor may store any new additional suggestion models asadditions to the existing set of suggestion models. At 3146, theprocessor may store any new additional feature vectors, context vectorsand/or action indications as additions to their respective existingsets. At 3148, the processor may check whether the received instance ofupdate data includes any new addition and/or replacement featureroutines. If so, then the processor may proceed with storing any newreplacement feature routines at 3150.

However, if at 3148, the received instance of update data does notinclude any new additional or replacement feature routines, then at3170, the processor may transmit the training data structure to each ofthe node devices of the set of node devices. At 3172, the processor maydistribute, among the set of node devices, any new additional and/orreplacement suggestion models received in the instance of update data,as well as any suggestion models associated with any new additionaland/or new replacement feature vectors, context vectors and/or actionindications received in the instance of update data. At 3174, theprocessor may retrieve the newly re-trained suggestion models from theset of node devices.

FIGS. 20A, 20B and 20C, together, illustrate an example embodiment of alogic flow 3200. The logic flow 3200 may be representative of some orall of the operations executed by one or more embodiments describedherein. More specifically, the logic flow 3200 may illustrate operationsperformed by the processor 2550 in executing the control routine 2540,and/or performed by other component(s) of the coordinating device 2500.Alternatively, the logic flow 3200 may illustrate operations performedby the processor 2350 in executing the control routine 2340, and/orperformed by other component(s) of a node device 2300 that is performingthe functions of the coordinating device 2500.

At 3210, a processor of a coordinating device of a distributedprocessing system (e.g., the processor 2550 of the coordinating device2500 of the distributed processing system 2000) may receive, via anetwork, an indication of the availability (e.g., arrival) of a data setthat is to be subjected to one or more data preparation actions (e.g.,via the network 2999, an indication of availability of a data set 2130).As has been discussed, the indication of availability may be receivedfrom various sources, including and not limited to, the one or morestorage devices in which the data set is stored (e.g., the one or morestorage devices 2100) or a viewing device operated by a user whoprovides descriptive information concerning the data set that is neededto search for and retrieve it.

At 3212, the processor may also receive indications of the context ofthe data set. As has been discussed, there may be multiple sources ofcontext information concerning a data set, including from among theinformation received from the viewing device as part of the request toaccess it, from the one or more storage devices, from another device viathe network, etc. Accordingly, at 3214, the processor may retrievefurther indications of contextual aspects of the data set from the oneor more storage devices. At 3216, the processor may select and/orgenerate a context vector for the data set for inclusion in an operatingdata structure based on the gathered indications of contextual aspectsof the data set.

At 3220, the processor may at least coordinate the distribution of dataset portions (e.g., the data set portions 2131) of the data set among aset of node devices selected to perform feature detection, and maytransmit the existing set of feature routines to each node device of theset of node devices at 3221 to enable each to perform feature detectionon data set portions of the data set at least partially in parallel. Asthe set of node devices perform feature detection, at 3223, theprocessor may receive indications from the set of node devices offeatures that have been detected in each of the data set portions. Ashas been discussed, the processor may engage in, or at least coordinate,exchanges of indications among the set of node devices of features thathave been detected to support instances in which the detection of onefeature has a dependency on the detection of another feature by what maybe a different a node device. At 3225, the processor may generate afeature vector for the data set for inclusion in the operating datastructure based on the received indications of detected features.

At 3230, the processor may transmit the operating data structure withthe newly generated context and feature vectors for the data set thereinto each of the node devices of the set of node devices. At 3231, theprocessor may distribute the set of suggestion models among the set ofnode devices to enable their use with the operating data structure todetermine what data preparation operations (i.e., what actions) are tobe suggested to be performed on the data set. At 3233, the processor mayreceive indications of what subset of the set of data preparationoperations to suggest be performed on the data set, and at 3235, maygenerate a suggested selections data indicative of that subset based onthose received indications.

At 3240, the processor may transmit the suggested selections data to aviewing device (e.g., the viewing device 2700) to enable a presentationof the suggested subset of data preparation operations to be made to auser of the viewing device. At 3242, the processor may receive, from theviewing device, an observed selections data that is indicative of asubset of the set of data preparation operations that the user selectedfor performance on the data set.

At 3250, the processor may coordinate the performance of the selectedsubset of data preparation operations on the data set by the set of nodedevices. As previously discussed, it may be the processor is able tocoordinate such a performance by the set of node devices using the dataset portions that were already earlier distributed thereamong forfeature detection, and/or may be able to coordinate such a performanceto be at least partially in parallel among the set of node devices. At3252, the processor may receive indications of completion of the datapreparation operations from the set of node devices, and may transmit anindication of such completion to the viewing device at 3254.

At 3260, and at least partially in parallel with the performance of theselected subset of data preparation operations on the data set at 3250,the processor may compare the selected subset of data preparationobjects (as selected by the user) to the suggested subset of datapreparation operations (as suggested to the user). At 3262, based on theresults of that comparison and/or based on whatever type of machinelearning algorithm (or portion thereof) that may be selected to controlthe occurrences of any additions being made to the training datastructure (e.g., the training data structure 2310), the processor may ormay not augment the training data structure with the combination of thefeature vector, the context vector and indication(s) of what datapreparation operations were selected by the user.

At 3270, the processor may check whether a predetermined interval oftime and/or interval of a quantity of times that a suggested subset ofdata preparation operations has been generated. If not, then at 3272,the processor may transmit the training data structure to each of thenode devices of the set of node devices. At 3273, the processor maydistribute the set of suggestion models among the set of node devices toenable a re-training of the set of suggestion models by the set of nodedevices based on the training data structure. At 3275, the processor mayretrieve the set of newly re-trained suggestion models from the set ofnode devices.

In various embodiments, each of the processors 2150, 2350, 2550 and 2750may include any of a wide variety of commercially available processors.Further, one or more of these processors may include multipleprocessors, a multi-threaded processor, a multi-core processor (whetherthe multiple cores coexist on the same or separate dies), and/or amulti-processor architecture of some other variety by which multiplephysically separate processors are linked.

In various embodiments, each of the control routines 2140, 2340, 2540and 2740, including the components of which each is composed, may beselected to be operative on whatever type of processor or processorsthat are selected to implement applicable ones of the processors 2150,2350, 2550 and/or 2750 within each one of the devices 2100, 2300, 2500and/or 2700, respectively. In various embodiments, each of theseroutines may include one or more of an operating system, device driversand/or application-level routines (e.g., so-called “software suites”provided on disc media, “applets” obtained from a remote server, etc.).Where an operating system is included, the operating system may be anyof a variety of available operating systems appropriate for theprocessors 2150, 2350, 2550 and/or 2750. Where one or more devicedrivers are included, those device drivers may provide support for anyof a variety of other components, whether hardware or softwarecomponents, of the devices 2100, 2300, 2500 and/or 2700.

In various embodiments, each of the storages 2160, 2360, 2560 and 2760may be based on any of a wide variety of information storagetechnologies, including volatile technologies requiring theuninterrupted provision of electric power, and/or including technologiesentailing the use of machine-readable storage media that may or may notbe removable. Thus, each of these storages may include any of a widevariety of types (or combination of types) of storage device, includingwithout limitation, read-only memory (ROM), random-access memory (RAM),dynamic RAM (DRAM), Double-Data-Rate DRAM (DDR-DRAM), synchronous DRAM(SDRAM), static RAM (SRAM), programmable ROM (PROM), erasableprogrammable ROM (EPROM), electrically erasable programmable ROM(EEPROM), flash memory, polymer memory (e.g., ferroelectric polymermemory), ovonic memory, phase change or ferroelectric memory,silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic or opticalcards, one or more individual ferromagnetic disk drives, non-volatilestorage class memory, or a plurality of storage devices organized intoone or more arrays (e.g., multiple ferromagnetic disk drives organizedinto a Redundant Array of Independent Disks array, or RAID array). Itshould be noted that although each of these storages is depicted as asingle block, one or more of these may include multiple storage devicesthat may be based on differing storage technologies. Thus, for example,one or more of each of these depicted storages may represent acombination of an optical drive or flash memory card reader by whichprograms and/or data may be stored and conveyed on some form ofmachine-readable storage media, a ferromagnetic disk drive to storeprograms and/or data locally for a relatively extended period, and oneor more volatile solid state memory devices enabling relatively quickaccess to programs and/or data (e.g., SRAM or DRAM). It should also benoted that each of these storages may be made up of multiple storagecomponents based on identical storage technology, but which may bemaintained separately as a result of specialization in use (e.g., someDRAM devices employed as a main storage while other DRAM devicesemployed as a distinct frame buffer of a graphics controller).

However, in a specific embodiment, the storage 2160 in embodiments inwhich the one or more of the storage devices 2100 store data sets 2130may be implemented with a redundant array of independent discs (RAID) ofa RAID level selected to provide fault tolerant storage.

In various embodiments, each of the input device(s) 2710 may each be anyof a variety of types of input device that may each employ any of a widevariety of input detection and/or reception technologies. Examples ofsuch input devices include, and are not limited to, microphones, remotecontrols, stylus pens, card readers, finger print readers, virtualreality interaction gloves, graphical input tablets, joysticks,keyboards, retina scanners, the touch input components of touch screens,trackballs, environmental sensors, and/or either cameras or cameraarrays to monitor movement of persons to accept commands and/or dataprovided by those persons via gestures and/or facial expressions.

In various embodiments, each of the display(s) 2780 may each be any of avariety of types of display device that may each employ any of a widevariety of visual presentation technologies. Examples of such a displaydevice includes, and is not limited to, a cathode-ray tube (CRT), anelectroluminescent (EL) panel, a liquid crystal display (LCD), a gasplasma display, etc. In some embodiments, the displays 2180 and/or 2880may each be a touchscreen display such that the input device(s) 2810,respectively, may be incorporated therein as touch-sensitive componentsthereof.

In various embodiments, each of the network interfaces 2190, 2390, 2590and 2790 may employ any of a wide variety of communications technologiesenabling these devices to be coupled to other devices as has beendescribed. Each of these interfaces includes circuitry providing atleast some of the requisite functionality to enable such coupling.However, each of these interfaces may also be at least partiallyimplemented with sequences of instructions executed by correspondingones of the processors (e.g., to implement a protocol stack or otherfeatures). Where electrically and/or optically conductive cabling isemployed, these interfaces may employ timings and/or protocolsconforming to any of a variety of industry standards, including withoutlimitation, RS-232C, RS-422, USB, Ethernet (IEEE-802.3) or IEEE-1394.Where the use of wireless transmissions is entailed, these interfacesmay employ timings and/or protocols conforming to any of a variety ofindustry standards, including without limitation, IEEE 802.11a,802.11ad, 802.11ah, 802.11ax, 802.11b, 802.11g, 802.16, 802.20 (commonlyreferred to as “Mobile Broadband Wireless Access”); Bluetooth; ZigBee;or a cellular radiotelephone service such as GSM with General PacketRadio Service (GSM/GPRS), CDMA/1×RTT, Enhanced Data Rates for GlobalEvolution (EDGE), Evolution Data Only/Optimized (EV-DO), Evolution ForData and Voice (EV-DV), High Speed Downlink Packet Access (HSDPA), HighSpeed Uplink Packet Access (HSUPA), 4G LTE, etc.

However, in a specific embodiment, one or more of the network interfaces2290, 2490, 2590 and/or 2890 may be implemented with multiplecopper-based or fiber-optic based network interface ports to provideredundant and/or parallel pathways in exchanging one or more of theportions of data of the data sets 2130, one or more of the metadataportions 2336, and/or one or more of the normalized metadata portions2436.

In various embodiments, the division of processing and/or storageresources among the federated devices 1500, and/or the API architecturesemployed to support communications between the federated devices andother devices may be configured to and/or selected to conform to any ofa variety of standards for distributed processing, including withoutlimitation, IEEE P2413, AllJoyn, IoTivity, etc. By way of example, asubset of API and/or other architectural features of one or more of suchstandards may be employed to implement the relatively minimal degree ofcoordination described herein to provide greater efficiency inparallelizing processing of data, while minimizing exchanges ofcoordinating information that may lead to undesired instances ofserialization among processes.

Some systems may use Hadoop®, an open-source framework for storing andanalyzing big data in a distributed computing environment. Some systemsmay use cloud computing, which can enable ubiquitous, convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, servers, storage, applications and services)that can be rapidly provisioned and released with minimal managementeffort or service provider interaction. Some grid systems may beimplemented as a multi-node Hadoop® cluster, as understood by a personof skill in the art. Apache™ Hadoop® is an open-source softwareframework for distributed computing.

The invention claimed is:
 1. An apparatus comprising a processor and astorage to store instructions that, when executed by the processor,cause the processor to perform operations comprising: receive updatedata comprising a feature routine, wherein the feature routine comprisesexecutable instructions to detect a structural feature of a data setfrom among a pre-selected set of structural features or a data featureof a data value of the data set from among a pre-selected set of datafeatures; coordinate a performance of a distribution of a set oftraining data set portions of a training data set among a set ofprocessor cores; provide a set of feature routines to each processorcore of the set of processor cores to enable each processor core of theset of processor cores to execute instructions of each feature routineto detect a structural feature of a corresponding training data setportion of the set of training data set portions or a data feature ofdata values of the corresponding training data set portion; receiveindications of detected structural features and detected data featuresof the set of training data set portions from the set of processorcores; generate, for the training data set, training metadata indicativeof the detected structural features and detected data features of theset of training data set portions from the set of processor cores;provide the training metadata, a training context data and a set ofaction indications to each processor core of the set of processor cores,wherein: the training context data is indicative of a pre-selected setof contextual aspects; and the set of action indications is indicativeof a subset of data preparation operations of a set of data preparationoperations to be suggested to be performed on the training data setbased on the training metadata and the training context data; distributea set of suggestion models among the set of processor cores to provideeach processor core of the set of processor cores with a differentsuggestion model from among the set of suggestion models, and to enablethe set of processor cores to re-train the set of suggestion models,wherein: each suggestion model comprises a pre-selected type of modelpreviously trained to determine whether to suggest that a correspondingdata preparation operation of the set of data preparation actions beperformed on a data set based on corresponding metadata and contextdata; the corresponding metadata is indicative of detected structuralfeatures and data features of the data set; and the correspondingcontext data is indicative of contextual aspects of the data set;receive, from each processor core, a corresponding suggestion model ofthe set of suggestion models after the re-training; and store thereceived suggestion models as replacements for the set of suggestionmodels.
 2. The apparatus of claim 1, wherein: space is allocated withinthe training metadata to store a separate indication of whether eachstructural feature of the set of structural features or data feature ofthe set of data features that is detected in the training data by acorresponding feature routine of the set of feature routines; and theprocessor is caused to perform operations comprising: determine whetherthe structural feature or data feature detected by the received featureroutine comprises a structural feature that is already among the set ofstructural features or a data feature that is already among the set ofdata features; and in response to a determination that the structuralfeature or data feature detected by the received feature routinecomprises a new structural feature that is not already among the set ofstructural features or a new data feature that is not already among theset of data features, perform operations comprising: store the receivedfeature routine as an addition to the set of feature routines prior tothe provision of the set of feature routines to each processor core ofthe set of processor cores to detect structural features and datafeatures of the training data set; and allocate another space in thetraining metadata to store a separate indication of whether the newstructural feature or new data feature is detected in the training data.3. The apparatus of claim 2, wherein the processor is caused to, inresponse to a determination that the structural feature or data featuredetected by the received feature routine comprises a structural featurethat is already among the set of structural features or a data featurethat is already among the set of data features, replace a correspondingfeature routine among the set of feature routines that detects thestructural feature or data feature with the received feature routineprior to the provision of the set of feature routines to the set ofprocessor cores to detect features of the training data set.
 4. Theapparatus of claim 1, wherein: for each data preparation operation ofthe set of data preparation operations, space is allocated within theset of action indications for a corresponding subset of actionindications; for each subset of action indications, each actionindication corresponds to a different combination of training data setand context data; the update data comprises a suggestion model; and theprocessor is caused to perform operations comprising: determine whetherthe data preparation operation that corresponds to the receivedsuggestion model comprises a data preparation operation that is alreadyamong the set of data preparation operations; and in response to adetermination that the corresponding data preparation operationcomprises a new data preparation operation that is not already among theset of data preparation operations, perform operations comprising: storethe suggestion model as an addition to the set of suggestion modelsprior to the distribution of the set of suggestion models among the setof processor cores to enable re-training of the set of suggestionmodels; and allocate another space within the set of action indicationsfor another subset of action indications that corresponds to the datapreparation operation that corresponds to the received suggestion model.5. The apparatus of claim 4, wherein the processor is caused to, inresponse to a determination that the corresponding data preparationoperation comprises a data preparation operation that is already amongthe set of data preparation operations, replace a correspondingsuggestion model among the set of suggestion models that determineswhether to suggest the data preparation operation prior to thedistribution of the set of suggestion models among the set of processorcores to enable re-training of the set of suggestion models.
 6. Theapparatus of claim 1, wherein: at least one suggestion model of the setof suggestion models comprises neural network configuration data thatcomprises an indication of at least one of a quantity of neurons, aquantity of rows of neurons, a set of connections among neurons, atrigger function to be implemented by at least one neuron, weights andbiases of the trigger function; and use of the at least one model by atleast one processor core of the set of processor cores to determinewhether to suggest that the corresponding data preparation operation beperformed on the data set comprises use of the configuration data by theat least one processor core to configure a neural network to implement aset of classifiers.
 7. The apparatus of claim 6, wherein the re-trainingof the at least one suggestion model comprises use, by the at least oneprocessor core, of at least a combination of the training metadata, thetraining context data and a subset of action indications of the set ofaction indications that corresponds to the corresponding datapreparation action to perform backpropagation on the neural network tochange at least one of the weights or the biases.
 8. The apparatus ofclaim 1, wherein: the set of processor cores is distributed among a setof node devices; the processor is caused to perform operationscomprising: receive indications of availability of each node device of aplurality of node devices; and select the set of node devices from amongthe plurality of node devices based on the received indications ofavailability; the coordination of the performance of distribution of theset of training data set portions of the training data set among the setof processor cores comprises a coordination of a performance of adistribution of the set of training data set portions among the set ofnode devices from at least one storage device through a network; theprovision of the set of feature routines to each processor core of theset of processor cores comprises transmission of the set of featureroutines to each node device of the set of node devices; the provisionof the training metadata, the training context data and the set ofaction indications to each processor core of the set of processor corescomprises transmission of the training metadata, the training contextdata and the set of action indications to each node device of the set ofnode devices; and the distribution of the set of suggestion models amongthe set of processor cores comprises the distribution of the set ofsuggestion models among the set of node devices to provide each nodedevice of the set of node devices with a different suggestion model fromamong the set of suggestion models.
 9. The apparatus of claim 8, whereinthe processor is caused to perform operations comprising: receive anindication of availability of the data set, wherein the indication ofavailability of the data set comprises an indication of at least one ofa contextual aspect of the data set from among the set of contextualaspects, a structural feature of the data set from among the set ofstructural features, or a data feature of a data value of the data setfrom among the set of data features; coordinate a performance of adistribution of a set of data set portions of the data set among the setof node devices; transmit the set of feature routines to each nodedevice of the set of node devices to enable each node device of the setof node devices to detect a structural feature of a corresponding dataset portion of the set of data set portions or a data feature of datavalues of the corresponding data set portion; receive indications ofdetected structural features and detected data features of the set ofdata set portions from the set of node devices; generate, for the dataset, metadata indicative of the detected structural features and thedetected data features; generate, for the data set, context dataindicative of the set of contextual aspects of the data set; transmitthe metadata and context data to each node device of the set of nodedevices; distribute the set of suggestion models among the set of nodedevices to provide each node device of the set of node devices with adifferent suggestion model from among the set of suggestion models, andto enable the set of node devices to employ the set of suggestion modelsto derive a suggested subset of data preparation operations of a set ofdata preparation operations to be suggested to be performed on the dataset; receive indications of the suggested subset from the set of nodedevices; transmit an indication of the suggested subset to a viewingdevice to enable a presentation of the suggested subset; receive, fromthe viewing device, an indication of a selected subset of the set ofdata preparation operations selected to be performed; compare theselected subset to the suggested subset to determine whether there is adifference between the suggested and selected subsets; and in responseto a determination that there is a difference between the suggested andselected subsets, and in preparation for the re-training of the set ofsuggestion models, perform operations comprising: store the metadata asan addition to the training metadata; store the context data as anaddition to the training context data; and store an indication of theselected subset as an addition to the set of action indications.
 10. Theapparatus of claim 1, wherein: the pre-selected set of contextualaspects comprises at least one of: an indication of an identity of asource of the data set; an indication of a location associated with thesource; an indication of an industry associated with the source; anindication of a time or date of receipt of the data set; an indicationof a user of a device from which a request is received to access thedata set; an indication of a location associated with the user; anindication of an industry associated with the user; or an indication ofa time or date of receipt of the request from the user to access thedata set; the pre-selected set of structural features comprises at leastone of: an indication of a size of the data set; an indication of a timeor date of generation of the data set; an indication of a language ofthe data set; an indication of an organization of data values within thedata set; or an indication of a number of dimensions of indexing of thedata set; and the pre-selected set of data features comprises at leastone of: an indication of at least one data type within the data set; anindication of at least one data format within the data set; or anindication of a minimum, a maximum, a mean or an average of a data valueof the data set.
 11. A computer-program product tangibly embodied in anon-transitory machine-readable storage medium, the computer-programproduct including instructions operable to cause a processor to performoperations comprising: receive update data comprising a feature routine,wherein the feature routine comprises executable instructions to detecta structural feature of a data set from among a pre-selected set ofstructural features or a data feature of a data value of the data setfrom among a pre-selected set of data features; coordinate a performanceof a distribution of a set of training data set portions of a trainingdata set among a set of processor cores; provide a set of featureroutines to each processor core of the set of processor cores to enableeach processor core of the set of processor cores to executeinstructions of each feature routine to detect a structural feature of acorresponding training data set portion of the set of training data setportions or a data feature of data values of the corresponding trainingdata set portion; receive indications of detected structural featuresand detected data features of the set of training data set portions fromthe set of processor cores; generate, for the training data set,training metadata indicative of the detected structural features anddetected data features of the set of training data set portions from theset of processor cores; provide the training metadata, a trainingcontext data and a set of action indications to each processor core ofthe set of processor cores, wherein: the training context data isindicative of a pre-selected set of contextual aspects; and the set ofaction indications is indicative of a subset of data preparationoperations of a set of data preparation operations to be suggested to beperformed on the training data set based on the training metadata andthe training context data; distribute a set of suggestion models amongthe set of processor cores to provide each processor core of the set ofprocessor cores with a different suggestion model from among the set ofsuggestion models, and to enable the set of processor cores to re-trainthe set of suggestion models, wherein: each suggestion model comprises apre-selected type of model previously trained to determine whether tosuggest that a corresponding data preparation operation of the set ofdata preparation actions be performed on a data set based oncorresponding metadata and context data; the corresponding metadata isindicative of detected structural features and data features of the dataset; and the corresponding context data is indicative of contextualaspects of the data set; receive, from each processor core, acorresponding suggestion model of the set of suggestion models after there-training; and store the received suggestion models as replacementsfor the set of suggestion models.
 12. The computer-program product ofclaim 11, wherein: space is allocated within the training metadata tostore a separate indication of whether each structural feature of theset of structural features or data feature of the set of data featuresthat is detected in the training data by a corresponding feature routineof the set of feature routines; and the processor is caused to performoperations comprising: determine whether the structural feature or datafeature detected by the received feature routine comprises a structuralfeature that is already among the set of structural features or a datafeature that is already among the set of data features; and in responseto a determination that the structural feature or data feature detectedby the received feature routine comprises a new structural feature thatis not already among the set of structural features or a new datafeature that is not already among the set of data features, performoperations comprising: store the received feature routine as an additionto the set of feature routines prior to the provision of the set offeature routine to each processor core of the set of processor cores todetect structural features and data features of the training data set;and allocate another space in the training metadata to store a separateindication of whether the new structural feature or new data feature isdetected in the training data.
 13. The computer-program product of claim12, wherein the processor is caused to, in response to a determinationthat the structural feature or data feature detected by the receivedfeature routine comprises a structural feature that is already among theset of structural features or a data feature that is already among theset of data features, replace a corresponding feature routine among theset of feature routines that detects the structural feature or datafeature with the received feature routine prior to the provision of theset of feature routines to the set of processor cores to detect featuresof the training data set.
 14. The computer-program product of claim 11,wherein: for each data preparation operation of the set of datapreparation operations, space is allocated within the set of actionindications for a corresponding subset of action indications; for eachsubset of action indications, each action indication corresponds to adifferent combination of training data set and context data; the updatedata comprises a suggestion model; and the processor is caused toperform operations comprising: determine whether the data preparationoperation that corresponds to the received suggestion model comprises adata preparation operation that is already among the set of datapreparation operations; and in response to a determination that thecorresponding data preparation operation comprises a new datapreparation operation that is not already among the set of datapreparation operations, perform operations comprising: store thesuggestion model as an addition to the set of suggestion models prior tothe distribution of the set of suggestion models among the set ofprocessor cores to enable re-training of the set of suggestion models;and allocate another space within the set of action indications foranother subset of action indications that corresponds to the datapreparation operation that corresponds to the received suggestion model.15. The apparatus of claim 14, wherein the processor is caused to, inresponse to a determination that the corresponding data preparationoperation comprises a data preparation operation that is already amongthe set of data preparation operations, replace a correspondingsuggestion model among the set of suggestion models that determineswhether to suggest the data preparation operation prior to thedistribution of the set of suggestion models among the set of processorcores to enable re-training of the set of suggestion models.
 16. Thecomputer-program product of claim 11, wherein: at least one suggestionmodel of the set of suggestion models comprises neural networkconfiguration data that comprises an indication of at least one of aquantity of neurons, a quantity of rows of neurons, a set of connectionsamong neurons, a trigger function to be implemented by at least oneneuron, weights and biases of the trigger function; and use of the atleast one model by at least one processor core of the set of processorcores to determine whether to suggest that the corresponding datapreparation operation be performed on the data set comprises use of theconfiguration data by the at least one processor core to configure aneural network to implement a set of classifiers.
 17. Thecomputer-program product of claim 16, wherein the re-training of the atleast one suggestion model comprises use, by the at least one processorcore, of at least a combination of the training metadata, the trainingcontext data and a subset of action indications of the set of actionindications that corresponds to the corresponding data preparationaction to perform backpropagation on the neural network to change atleast one of the weights or the biases.
 18. The computer-program productof claim 11, wherein: the set of processor cores is distributed among aset of node devices; the processor is caused to perform operationscomprising: receive indications of availability of each node device of aplurality of node devices; and select the set of node devices from amongthe plurality of node devices based on the received indications ofavailability; the coordination of the performance of distribution of theset of training data set portions of the training data set among the setof processor cores comprises a coordination of a performance of adistribution of the set of training data set portions among the set ofnode devices from at least one storage device through a network; theprovision of the set of feature routines to each processor core of theset of processor cores comprises transmission of the set of featureroutines to each node device of the set of node devices; the provisionof the training metadata, the training context data and the set ofaction indications to each processor core of the set of processor corescomprises transmission of the training metadata, the training contextdata and the set of action indications to each node device of the set ofnode devices; and the distribution of the set of suggestion models amongthe set of processor cores comprises the distribution of the set ofsuggestion models among the set of node devices to provide each nodedevice of the set of node devices with a different suggestion model fromamong the set of suggestion models.
 19. The computer-program product ofclaim 18, wherein the processor is caused to perform operationscomprising: receive an indication of availability of the data set,wherein the indication of availability of the data set comprises anindication of at least one of a contextual aspect of the data set fromamong the set of contextual aspects, a structural feature of the dataset from among the set of structural features, or a data feature of adata value of the data set from among the set of data features;coordinate a performance of a distribution of a set of data set portionsof the data set among the set of node devices; transmit the set offeature routines to each node device of the set of node devices toenable each node device of the set of node devices to detect astructural feature of a corresponding data set portion of the set ofdata set portions or a data feature of data values of the correspondingdata set portion; receive indications of detected structural featuresand detected data features of the set of data set portions from the setof node devices; generate, for the data set, metadata indicative of thedetected structural features and the detected data features; generate,for the data set, context data indicative of the set of contextualaspects of the data set; transmit the metadata and context data to eachnode device of the set of node devices; distribute the set of suggestionmodels among the set of node devices to provide each node device of theset of node devices with a different suggestion model from among the setof suggestion models, and to enable the set of node devices to employthe set of suggestion models to derive a suggested subset of datapreparation operations of a set of data preparation operations to besuggested to be performed on the data set; receive indications of thesuggested subset from the set of node devices; transmit an indication ofthe suggested subset to a viewing device to enable a presentation of thesuggested subset; receive, from the viewing device, an indication of aselected subset of the set of data preparation operations selected to beperformed; compare the selected subset to the suggested subset todetermine whether there is a difference between the suggested andselected subsets; and in response to a determination that there is adifference between the suggested and selected subsets, and inpreparation for the re-training of the set of suggestion models, performoperations comprising: store the metadata as an addition to the trainingmetadata; store the context data as an addition to the training contextdata; and store an indication of the selected subset as an addition tothe set of action indications.
 20. The computer-program product of claim11, wherein: the pre-selected set of contextual aspects comprises atleast one of: an indication of an identity of a source of the data set;an indication of a location associated with the source; an indication ofan industry associated with the source; an indication of a time or dateof receipt of the data set; an indication of a user of a device fromwhich a request is received to access the data set; an indication of alocation associated with the user; an indication of an industryassociated with the user; or an indication of a time or date of receiptof the request from the user to access the data set; the pre-selectedset of structural features comprises at least one of: an indication of asize of the data set; an indication of a time or date of generation ofthe data set; an indication of a language of the data set; an indicationof an organization of data values within the data set; or an indicationof a number of dimensions of indexing of the data set; and thepre-selected set of data features comprises at least one of: anindication of at least one data type within the data set; an indicationof at least one data format within the data set; or an indication of aminimum, a maximum, a mean or an average of a data value of the dataset.
 21. A computer-implemented method comprising: receiving, by aprocessor, update data comprising a feature routine, wherein the featureroutine comprises executable instructions to detect a structural featureof a data set from among a pre-selected set of structural features or adata feature of a data value of the data set from among a pre-selectedset of data features; coordinating, by the processor, a performance of adistribution of a set of training data set portions of a training dataset among a set of processor cores; providing, by the processor, a setof feature routines to each processor core of the set of processor coresto enable each processor core of the set of processor cores to executeinstructions of each feature routine to detect a structural feature of acorresponding training data set portion of the set of training data setportions or a data feature of data values of the corresponding trainingdata set portion; receiving, by the processor, indications of detectedstructural features and detected data features of the set of trainingdata set portions from the set of processor cores; generating, by theprocessor, for the training data set, training metadata indicative ofthe detected structural features and detected data features of the setof training data set portions from the set of processor cores;providing, by the processor, the training metadata, a training contextdata and a set of action indications to each processor core of the setof processor cores, wherein: the training context data is indicative ofa pre-selected set of contextual aspects; and the set of actionindications is indicative of a subset of data preparation operations ofa set of data preparation operations to be suggested to be performed onthe training data set based on the training metadata and the trainingcontext data; distributing, by the processor, a set of suggestion modelsamong the set of processor cores to provide each processor core of theset of processor cores with a different suggestion model from among theset of suggestion models, and to enable the set of processor cores tore-train the set of suggestion models, wherein: each suggestion modelcomprises a pre-selected type of model previously trained to determinewhether to suggest that a corresponding data preparation operation ofthe set of data preparation actions be performed on a data set based oncorresponding metadata and context data; the corresponding metadata isindicative of detected structural features and data features of the dataset; and the corresponding context data is indicative of contextualaspects of the data set; receiving, by the processor and from eachprocessor core, a corresponding suggestion model of the set ofsuggestion models after the re-training; and storing the receivedsuggestion models as replacements for the set of suggestion models. 22.The computer-implemented method of claim 21, wherein: space is allocatedwithin the training metadata to store a separate indication of whethereach structural feature of the set of structural features or datafeature of the set of data features that is detected in the trainingdata by a corresponding feature routine of the set of feature routines;and the method further comprises: determining, by the processor, whetherthe structural feature or data feature detected by the received featureroutine comprises a structural feature that is already among the set ofstructural features or a data feature that is already among the set ofdata features; and in response to a determination that the structuralfeature or data feature detected by the received feature routinecomprises a new structural feature that is not already among the set ofstructural features or a new data feature that is not already among theset of data features, performing operations comprising: storing thereceived feature routine as an addition to the set of feature routinesprior to the provision of the set of feature routine to each processorcore of the set of processor cores to detect structural features anddata features of the training data set; and allocating another space inthe training metadata to store a separate indication of whether the newstructural feature or new data feature is detected in the training data.23. The computer-implemented method of claim 22, further comprising, inresponse to a determination that the structural feature or data featuredetected by the received feature routine comprises a structural featurethat is already among the set of structural features or a data featurethat is already among the set of data features, replacing acorresponding feature routine among the set of feature routines thatdetects the structural feature or data feature with the received featureroutine prior to the provision of the set of feature routines to the setof processor cores to detect features of the training data set.
 24. Thecomputer-implemented method of claim 21, wherein: for each datapreparation operation of the set of data preparation operations, spaceis allocated within the set of action indications for a correspondingsubset of action indications; for each subset of action indications,each action indication corresponds to a different combination oftraining data set and context data; the update data comprises asuggestion model; and the method further comprises: determining, by theprocessor, whether the data preparation operation that corresponds tothe received suggestion model comprises a data preparation operationthat is already among the set of data preparation operations; and inresponse to a determination that the corresponding data preparationoperation comprises a new data preparation operation that is not alreadyamong the set of data preparation operations, performing operationscomprising: storing the suggestion model as an addition to the set ofsuggestion models prior to the distribution of the set of suggestionmodels among the set of processor cores to enable re-training of the setof suggestion models; and allocating another space within the set ofaction indications for another subset of action indications thatcorresponds to the data preparation operation that corresponds to thereceived suggestion model.
 25. The computer-implemented method of claim24, further comprising, in response to a determination that thecorresponding data preparation operation comprises a data preparationoperation that is already among the set of data preparation operations,replacing a corresponding suggestion model among the set of suggestionmodels that determines whether to suggest the data preparation operationprior to the distribution of the set of suggestion models among the setof processor cores to enable re-training of the set of suggestionmodels.
 26. The computer-implemented method of claim 21, wherein: atleast one suggestion model of the set of suggestion models comprisesneural network configuration data that comprises an indication of atleast one of a quantity of neurons, a quantity of rows of neurons, a setof connections among neurons, a trigger function to be implemented by atleast one neuron, weights and biases of the trigger function; and use ofthe at least one model by at least one processor core of the set ofprocessor cores to determine whether to suggest that the correspondingdata preparation operation be performed on the data set comprises use ofthe configuration data by the at least one processor core to configure aneural network to implement a set of classifiers.
 27. Thecomputer-implemented method of claim 26, wherein the re-training of theat least one suggestion model comprises use, by the at least oneprocessor core, of at least a combination of the training metadata, thetraining context data and a subset of action indications of the set ofaction indications that corresponds to the corresponding datapreparation action to perform backpropagation on the neural network tochange at least one of the weights or the biases.
 28. Thecomputer-implemented method of claim 21, wherein: the set of processorcores is distributed among a set of node devices; the method furthercomprises: receiving, by the processor, indications of availability ofeach node device of a plurality of node devices; and selecting, by theprocessor, the set of node devices from among the plurality of nodedevices based on the received indications of availability; thecoordination of the performance of distribution of the set of trainingdata set portions of the training data set among the set of processorcores comprises a coordination of a performance of a distribution of theset of training data set portions among the set of node devices from atleast one storage device through a network; the provision of the set offeature routines to each processor core of the set of processor corescomprises transmission of the set of feature routines to each nodedevice of the set of node devices; the provision of the trainingmetadata, the training context data and the set of action indications toeach processor core of the set of processor cores comprises transmissionof the training metadata, the training context data and the set ofaction indications to each node device of the set of node devices; andthe distribution of the set of suggestion models among the set ofprocessor cores comprises the distribution of the set of suggestionmodels among the set of node devices to provide each node device of theset of node devices with a different suggestion model from among the setof suggestion models.
 29. The computer-implemented method of claim 28,further comprising: receiving, by the processor, an indication ofavailability of the data set, wherein the indication of availability ofthe data set comprises an indication of at least one of a contextualaspect of the data set from among the set of contextual aspects, astructural feature of the data set from among the set of structuralfeatures, or a data feature of a data value of the data set from amongthe set of data features; coordinating, by the processor and through anetwork, a performance of a distribution of a set of data set portionsof the data set among the set of node devices; transmitting, by theprocessor, the set of feature routines to each node device of the set ofnode devices to enable each node device of the set of node devices todetect a structural feature of a corresponding data set portion of theset of data set portions or a data feature of data values of thecorresponding data set portion; receiving, by the processor, indicationsof detected structural features and detected data features of the set ofdata set portions from the set of node devices; generating, by theprocessor, for the data set, metadata indicative of the detectedstructural features and the detected data features; generating, by theprocessor, for the data set, context data indicative of the set ofcontextual aspects of the data set; transmitting, by the processor, themetadata and context data to each node device of the set of nodedevices; distributing, by the processor, the set of suggestion modelsamong the set of node devices to provide each node device of the set ofnode devices with a different suggestion model from among the set ofsuggestion models, and to enable the set of node devices to employ theset of suggestion models to derive a suggested subset of datapreparation operations of a set of data preparation operations to besuggested to be performed on the data set; receiving, by the processor,indications of the suggested subset from the set of node devices;transmitting, by the processor and by the network, an indication of thesuggested subset to a viewing device to enable a presentation of thesuggested subset; receiving, by the processor, and via the network fromthe viewing device, an indication of a selected subset of the set ofdata preparation operations selected to be performed; comparing, by theprocessor, the selected subset to the suggested subset to determinewhether there is a difference between the suggested and selectedsubsets; and in response to a determination that there is a differencebetween the suggested and selected subsets, and in preparation for there-training of the set of suggestion models, performing operationscomprising: storing the metadata as an addition to the trainingmetadata; storing the context data as an addition to the trainingcontext data; and storing an indication of the selected subset as anaddition to the set of action indications.
 30. The computer-implementedmethod of claim 21, wherein: the pre-selected set of contextual aspectscomprises at least one of: an indication of an identity of a source ofthe data set; an indication of a location associated with the source; anindication of an industry associated with the source; an indication of atime or date of receipt of the data set; an indication of a user of adevice from which a request is received to access the data set; anindication of a location associated with the user; an indication of anindustry associated with the user; or an indication of a time or date ofreceipt of the request from the user to access the data set; thepre-selected set of structural features comprises at least one of: anindication of a size of the data set; an indication of a time or date ofgeneration of the data set; an indication of a language of the data set;an indication of an organization of data values within the data set; oran indication of a number of dimensions of indexing of the data set; andthe pre-selected set of data features comprises at least one of: anindication of at least one data type within the data set; an indicationof at least one data format within the data set; or an indication of aminimum, a maximum, a mean or an average of a data value of the dataset.